Image-matching-based perceptual authenticity colored flicker evaluation and determination system for color and appearance of effect pigment-containing coatings

Abstract

A computing system and related method for evaluating and matching a colored flickering appearance of a coating comprising at least one effect pigment type is disclosed. The method processes a digital image of a coating sample to obtain pixel-specific flicker point data and pixel-specific background data, both of which are formatted according to an advanced color appearance model corresponding to human color perception. A colored flicker visual scale value is calculated from pixel-specific data, wherein the visual scale value is indicative of a colored flicker characteristic of the coating sample. The visual scale value of the coating sample is compared to the visual scale value of a previously characterized candidate coating sample. The comparison result is used to determine a degree of matching between the candidate coating sample and the coating sample (in terms of colored flicker characteristics).

Description

Technical Field

[0001] In general, embodiments of the subject matter described herein relate to coating technology. More specifically, embodiments of the subject matter relate to systems and methods for evaluating and matching the colored, shimmering appearance of a sample coating to one or more known coating samples. Background Art

[0002] Visualization and selection of coatings with desired color and appearance play an important role in many applications. For example, coating suppliers must provide thousands of coatings to cover the coating ranges of global OEM manufacturers for all current and latest model vehicles. Providing such a large amount of different coatings as factory-packaged products increases the complexity of paint manufacturing and increases inventory costs. Therefore, paint suppliers provide a mixer system that typically includes 50 to 100 components (for example, single pigment coloring matter (pigment tint), binder, solvent, additive) and the coating formulation of each component that matches the coating range of the vehicle. The mixer can be stationed in a repair shop (that is, a body repair shop) or a paint dealer. The system allows the user to obtain a coating with desired color and appearance by distributing each component with the amount corresponding to a specific coating formulation. The coating formulation is usually stored in a database and distributed to the client via computer software by downloading or being directly connected to an Internet database. Every coating formulation typically relates to one or more alternative coating formulations to consider the coating variation caused by the change in vehicle production.

[0003] This variation complicates identifying the paint formulation that is most similar to the target coating sample. For example, a particular coating may appear on three vehicle models produced over a five-model-year period using paint from two OEM paint suppliers, in two assembly plants with different applicators. Effect coatings complicate coating matching due to spatially non-uniform color variations in visual texture or appearance, such as the color shimmer that is always present under directional lighting indoors (lighting rooms, etc.) and outdoors (sunlight, etc.). These sources of variation result in significant coating variations across the population of vehicles with that particular coating. Alternative paint formulations provided by the paint supplier are matched to a subset of the color population, allowing a close match (based on a combination of color and visual texture matching under different measurement geometries) to be obtained for any vehicle requiring repair.

[0004] Identifying the effect coating formulation that is most similar to the effect target coating sample for repair is typically accomplished using a multi-angle spectrophotometer or a fan deck. A multi-angle or multi-gonio spectrophotometer and an image capture system measure one or more color and appearance attributes of the target coating to be repaired. This color and appearance data is then compared with corresponding data from potential candidate formulations contained in a database. The candidate formulation whose color and appearance attributes best match those of the target coating to be repaired is then selected as the coating formulation that is most similar to the target coating.

[0005] Alternatively, a color strip chart includes multiple sample coatings on a page or sheet within the color strip chart. Each alternative paint formula can be represented by a color chip in the color strip chart, which allows the user to select the best matching formula by visually comparing the sample coatings of the color strip chart with the target coating sample to be matched under different measurement geometries (combinations of lighting and viewing angles) with the vehicle. The formula associated with the sample coating that best matches the color and appearance attributes of the target coating to be matched is then selected as the paint formula most similar to the target coating sample. However, color strip charts are cumbersome to use and difficult to maintain because a large number of sample coatings are required to account for all coatings on vehicles on the road today.

[0006] Optical determination of color and colored sparkle is essential to achieving a high-quality appearance match between two effect coatings. Colored sparkle is a visual effect caused by direct reflections from disorganized pigment flakes (behaving as full or partial micro-mirrors) (e.g., aluminum or pearlescent flakes) contained in the coating composition. The American Society for Testing and Materials (ASTM International) defines "sparkle" as: "a property of the appearance of a material that appears to emit or exhibit minute points of light that are significantly brighter than their immediate surroundings, which become more pronounced when at least one of the contributors (observer, specimen, light source) moves" (ASTM E284-17 - Standard Terminology of Appearance). Colored sparkle effects consist of random, dynamic patterns (visual textures) that appear under lighting conditions of varying directions. Under specific combinations of lamp / illumination position, specimen position, and observer position, the sparkle points are visible (with high contrast on a darker colored background) even when they are slightly displaced and / or tilted (individually or in combination).

[0007] Therefore, it is desirable to provide a system and method for matching the color and colored shimmer appearance of a coating containing at least one effect pigment type. Additionally, other desirable features and characteristics will become apparent from the following Summary and Detailed Description of the Invention and the appended claims, taken in conjunction with the accompanying drawings and background of the invention. Summary of the Invention

[0008] The present application discloses a computer-implemented method for evaluating and matching the colored flicker appearance of a coating containing at least one effect pigment type. An exemplary embodiment of the method includes: receiving a digital image of a coating sample to be colored flicker matched, the digital image being captured by a digital image capture system and being represented by captured color image data formatted according to a first color model inherent to the digital image capture system; converting the captured color image data into pixel-specific flicker point data and pixel-specific background data, wherein the pixel-specific flicker point data and the pixel-specific background data are both formatted according to an advanced color appearance model corresponding to human color perception; calculating a first colored flicker visual scale value for the coating sample to be colored flicker matched from the pixel-specific flicker point data and the pixel-specific background data, wherein the first colored flicker visual scale value indicates the colored flicker characteristics of the coating sample to be colored flicker matched; retrieving a second colored flicker visual scale value for a candidate coating sample, wherein the second colored flicker visual scale value indicates the colored flicker characteristics of the candidate coating sample; comparing the first colored flicker visual scale value to the second colored flicker visual scale value; and determining a degree of match between the candidate coating sample and the coating sample to be colored flicker matched based on a result of the comparing step.

[0009] The present invention also discloses a computing system operable to match the color and appearance of a coating containing at least one effect pigment type. An exemplary embodiment of the computing system includes: at least one processor; and at least one non-transitory machine-readable storage medium storing instructions executable by the at least one processor. The instructions can be configured to cause the at least one processor to execute a method comprising the following steps: receiving a digital image of a coating sample to be color-flicker matched, the digital image being captured by a digital image capture system and represented by captured color image data, the captured color image data being formatted according to a first color model inherent to the digital image capture system; converting the captured color image data into pixel-specific flicker point data and pixel-specific background data, wherein the pixel-specific flicker point data and the pixel-specific background data are both formatted according to an advanced color appearance model corresponding to human color perception; calculating a first color-flicker visual scale value of the coating sample to be color-flicker matched from the pixel-specific flicker point data and the pixel-specific background data, wherein the first color-flicker visual scale value indicates the color-flicker characteristics of the coating sample to be color-flicker matched; retrieving a second color-flicker visual scale value of a candidate coating sample, wherein the second color-flicker visual scale value indicates the color-flicker characteristics of the candidate coating sample; comparing the first color-flicker visual scale value with the second color-flicker visual scale value; and determining a degree of matching between the candidate coating sample and the coating sample to be color-flicker matched based on a result of the comparison step.

[0010] The present invention also discloses at least one non-transitory machine-readable storage medium storing instructions executable by at least one processor, wherein the instructions are configured to cause the at least one processor to perform a method for matching the colored shimmering appearance of a coating containing at least one effect pigment type. An exemplary embodiment of the method includes the following steps: receiving a digital image of a coating sample to be color-flicker matched, the digital image being captured by a digital image capture system, and the digital image being represented by captured color image data formatted according to a first color model inherent to the digital image capture system; converting the captured color image data into pixel-specific flicker point data and pixel-specific background data, wherein the pixel-specific flicker point data and the pixel-specific background data are both formatted according to an advanced color appearance model corresponding to human color perception; calculating a first color-flicker visual scale value of the coating sample to be color-flicker matched from the pixel-specific flicker point data and the pixel-specific background data, wherein the first color-flicker visual scale value indicates the color-flicker characteristics of the coating sample to be color-flicker matched; retrieving a second color-flicker visual scale value of a candidate coating sample, wherein the second color-flicker visual scale value indicates the color-flicker characteristics of the candidate coating sample; comparing the first color-flicker visual scale value with the second color-flicker visual scale value; and determining the degree of matching between the candidate coating sample and the coating sample to be color-flicker matched based on the result of the comparison step.

[0011] This summary is intended to introduce some concepts in a simplified form that will be further described in the detailed description below. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] A more complete understanding of the present subject matter may be obtained by referring to the detailed description and claims when considered in conjunction with the following drawings, wherein like reference numerals represent similar elements throughout the drawings.

[0013] Figure 1 is a simplified block diagram of a system for evaluating and matching the color and appearance of coatings according to an exemplary embodiment of the present invention;

[0014] Figure 2 is a simplified block diagram and data flow diagram illustrating a method of evaluating and matching the color and appearance of a coating according to an exemplary embodiment of the present invention;

[0015] Figure 3 is a block diagram of an exemplary embodiment of a computer-based device;

[0016] Figure 4is a flow chart illustrating an exemplary embodiment of a method for visually scaling the colored, sparkling appearance of a coating containing at least one effect pigment type;

[0017] Figure 5 is corresponding to Figure 4 Block diagrams and data flow diagrams of processes consistent with the illustrated method;

[0018] Figure 6 is a graph including a graph of different colored flicker vision scale values;

[0019] Figure 7 is a graph including a graph of different colored sparkle visual scale values ​​and labels corresponding to the coating sample (with its visual colored sparkle tolerance region) and the candidate coating sample;

[0020] Figure 8 is a flow chart illustrating an exemplary embodiment of a method for matching the colored shimmering appearance of a coating containing at least one effect pigment type;

[0021] Figure 9 is a diagram illustrating a method of calculating a minimum color difference index from pixel-based scintillation point color model attributes of a coating sample and a candidate coating sample. DETAILED DESCRIPTION

[0022] The following detailed description is illustrative only and is not intended to limit the embodiments of the present invention or the application and use of such embodiments. As used herein, the word "exemplary" means "serving as an example, instance, or illustration." Any embodiment described herein as exemplary is not necessarily to be construed as superior or advantageous over other embodiments. Furthermore, no intention is to be bound by any expressed or implied theory presented in the preceding technical field, background, summary, or the following detailed description.

[0023] In this article, processes and techniques can be described from the perspective of functional and / or logical block components and with reference to the symbolic representation of operations, processing tasks and functions that can be performed by various computing components or devices. These operations, tasks and functions are sometimes referred to as computer-executable, computerized, software-implemented or computer-implemented. It should be understood that the various block components shown in the figure can be implemented by any number of hardware, software and / or firmware components configured to perform the specified functions. For example, the embodiment of the system or component can use various integrated circuit components, such as storage elements, digital signal processing elements, logic elements, lookup tables, etc., which can perform various functions under the control of one or more microprocessors or other control devices.

[0024] When implemented in software or other manner, the various elements of the systems and devices described herein are essentially code segments or instructions that cause one or more processor devices to perform various tasks. In certain embodiments, the program or code segments are stored in at least one tangible processor-readable medium, which can include any medium that can store or transmit information. Examples of non-transitory processor-readable media include electronic circuits, semiconductor memory devices, ROMs, flash memories, erasable ROMs (EROMs), floppy disks, CD-ROMs, optical disks, hard disks, and the like.

[0025] As used herein, the term "pigment" or "pigments" refers to one or more colorants that produce one or more colors. Pigments can be derived from natural or synthetic sources and can be made from organic or inorganic ingredients. Pigments can also include metallic particles or flakes of specific or mixed shapes and dimensions. Pigments are generally insoluble in the coating composition.

[0026] The term "effect pigment" or "effect pigments" refers to pigments that produce special spatially inhomogeneous effects in a coating, i.e., color flop and visual texture (sparkle and graininess). Examples of effect pigments include, but are not limited to, light-scattering pigments, light-interference pigments, and light-reflecting pigments. Metal flakes such as aluminum flakes and pearlescent pigments such as pigments based on natural or synthetic mica, Xirallic pigments, and colored aluminum flakes are examples of effect pigments.

[0027] The term "appearance" may include: (1) aspects of the visual experience through which a coating is viewed or recognized; and (2) the perception that integrates spectral and geometric aspects of a coating with its lighting and viewing environment. Typically, appearance includes the texture, roughness or graininess, sparkle, or other visual effects of a coating, particularly when viewed from varying viewing angles and / or under varying lighting conditions. Appearance characteristics or appearance data may include, but are not limited to, descriptive or measured data regarding texture, metallic effects, pearlescent effects, gloss, image differences, flake appearance and size (such as texture, roughness or graininess, sparkle, shimmer, and glitter), and the enhancement of depth perception in a coating imparted by flakes, particularly produced by metallic and pearlescent flakes. Appearance characteristics may be obtained by visual inspection or by using an appearance measurement device.

[0028] The term "color data" or "color characteristics" or "color model attributes" of a coating may include measured color data including, but not limited to: spectral reflectance values; X, Y, Z values; , , value; , , hab value; or a combination thereof. The color data may also include the color code of the vehicle, the color name or description, or a combination thereof. The color data may even include visual aspects of the color, chromaticity, hue, lightness or darkness of the coating. The color data may be obtained by visual inspection or by using a color measuring device such as a colorimeter, spectrophotometer or angle spectrophotometer. In particular, a spectrophotometer obtains color data by determining the wavelength of light reflected by the coating. The color data may also include: descriptive data, such as the name of the color, the color code of the vehicle; binary, texture or encrypted data files containing descriptive data of one or more colors; measurement data files, such as those generated by a color measuring device; or export / import data files generated by a computing device or a color measuring device. The color data may also be generated by an appearance measuring device or a color-appearance dual-function measuring device.

[0029] The term "coating" or "coating composition" may include any coating composition known to those skilled in the art and may include: a two-component coating composition, also known as a "2K coating composition"; a one-component or 1K coating composition; a coating composition having a crosslinkable component and a crosslinking component; a radiation-curable coating composition, such as a UV-curable coating composition or an electron beam-curable coating composition; a single-cure coating composition; a dual-cure coating composition; a lacquer coating composition; a waterborne coating composition or an aqueous coating composition; a solvent-borne coating composition; or any other coating composition known to those skilled in the art. The coating composition may be formulated as a primer, basecoat, midcoat, clearcoat, or tinted coating composition by incorporating the desired pigment or effect pigment. The coating composition may also be formulated as a clearcoat composition.

[0030] The terms "recipe," "matching recipe," or "matching recipe" with respect to a coating composition refer to a collection of information or instructions based on which a coating composition can be prepared. In one example, a matching recipe includes a list of the names and quantities of solid pigments, effect pigments, and other components of the coating composition. In another example, a matching recipe includes instructions on how to mix the various components of the coating composition.

[0031] The subject matter described herein relates to a system and related methods for analyzing the visual appearance of coatings, such as coatings exhibiting colored sparkle properties. One or more of the techniques described herein can be used to compare coatings containing at least one type of effect pigment that produces colored sparkle. As a different example, if two colored sparkle coating samples exhibit similar sparkle density, similar high color contrast between the sparkle point and the darker background of the non-sparkle point, and similar colored sparkle color gamut, the two samples can be described as having similar or matching colored sparkle appearances because their coating formulations contain similar effect pigment types and similar concentrations. In certain embodiments, a colored sparkle model simulates human visual perception when comparing two colored sparkle samples under different lighting and viewing conditions. High dynamic range (HDR) color images are used to accurately encode the rich color diversity of colored sparkle patterns without losing valuable image information. The color difference between the sparkle point and the background point under representative patch field conditions (determined using an advanced color appearance model or a human color perception model) is calculated to derive sparkle texture and sparkle intensity metrics for use in colored sparkle visual scaling or assessment. When comparing two coating samples, a multiple screening method is employed to predict a visual match determination (whether the colored sparkle appearance of the two samples is poorly or highly matched). The match determination is preferably based on the calculated color flicker texture and flicker intensity differences, visual tolerances, and color flicker gamut comparisons. According to certain embodiments, the comparison results, which consist of three queries (color flicker scale value difference; spatial pattern or macro texture difference; color flicker gamut or myopic texture difference), are used to determine how well the candidate coating sample matches the coating sample (in terms of color flicker characteristics).

[0032] Colored flicker can be measured and visually scaled using a computer-implemented system, the configuration and operation of which are described in detail below. Flicker (which can be achromatic or colored) is visually perceived as multiple high-contrast flicker points on a darker background (at these points, light is reflected to the observer from a thin sheet with disordered orientation, but these sheets behave like full or partial micromirrors). The main visual attributes of achromatic flicker are: flicker point density; and high contrast average value. The main visual attributes of colored flicker are: flicker point density; high color contrast average value; and the color gamut palette of a single flicker point (bluish, reddish, greenish, etc.). For example, two colored flicker coating samples may be visually characterized by: similar flicker density, similar high color contrast appearance between flicker points and a darker colored background, but different colored flicker color gamuts (for example, one coating sample primarily presents greenish flicker points, while the other coating sample presents a combination of greenish and reddish flicker points). For this example, different colored flicker color gamuts make the two coating samples visually appear dissimilar. Therefore, the system will determine that the two coating samples are poorly matched because their coating formulations have different effect pigment types. As a different example, if two colored glitter coating samples exhibit similar glitter density, similar high color contrast between the glitter dots and the darker colored background, and similar colored glitter color gamut, then the two samples can be described as having similar or matching colored glitter appearances because their coating formulations have similar effect pigment types and similar concentrations.

[0033] Figure 1 is a simplified block diagram of an embodiment of a system 100 for evaluating and matching the color and appearance of coatings (e.g., coatings containing at least one effect pigment type). For the illustrated embodiment, the system 100 is used to find at least one candidate coating sample (assuming one is available) that best matches the color and appearance of a coating sample 102 to be matched. The simplified embodiment of the system 100 depicted includes, but is not limited to: at least one digital image capture system 104; at least one computing system 106; and at least one coating sample database 108. For simplicity, one digital image capture system 104, one computing system 106, and one coating sample database 108 are shown and described. It should be understood that implementations of the system 100 may include more than one of these elements, as appropriate for a particular embodiment. The elements of the system 100 communicate with each other (via physical and / or wireless communication links) as needed to support the described functionality of the system 100.

[0034] Coating sample 102 may be located on substrate 110. Substrate 110 may be a vehicle, a vehicle component, a vehicle surface, or the like. Substrate 110 may also be any coated article that includes or carries coating sample 102. For this particular example, coating sample 102 is a colored coating that includes at least one effect pigment to provide a colored, shimmering appearance. Coating sample 102 is formed according to a specific production protocol that specifies the amounts and types of components used to create coating sample 102.

[0035] The digital image capture system 104 includes one or more digital cameras operable to capture at least one digital image of the coating sample 102. In a preferred embodiment, the digital image capture system 104 captures high-resolution, high dynamic range (HDR) images. Furthermore, the digital image capture system 104 is suitably configured and operable to capture digital images using multiple colorimetric angles (e.g., four different angles). Figure 1 The dashed line in represents the field of view of the captured digital image of the coating sample 102. The digital image captured by the digital image capture system 104 is processed by the computing system 106. In some embodiments, the digital image capture system 104 is separate and distinct from the computing system 106. In other embodiments, the digital image capture system 104 and the computing system 106 are combined and implemented together in a single piece of hardware.

[0036] The system 100 includes or cooperates with a coating sample database 108 containing data corresponding to a plurality of coating samples. As explained in more detail below, the coating sample database 108 can be maintained to store color image data, color model attribute data, histogram information derived from the color image data, appearance data, and / or other characterization data for any number of previously analyzed and characterized coating samples. The coating sample database 108 can be associated with the digital image capture system 104, associated with the computing system 106, and / or associated with a system or platform that is independent of and distinct from the digital image capture system 104 and the computing system 106, such as in a server-based environment or in a cloud computing environment.

[0037] The computing system 106 executes at least one matching algorithm to compare the colored shimmer characteristics of the coating sample 102 with the corresponding colored shimmer characteristics of previously characterized coating samples, with the goal of identifying a candidate coating sample that best matches the colored shimmer appearance of the coating sample 102. To this end, the computing system 106 can be suitably configured to generate and output an appropriate output 112 that identifies the best matching candidate coating sample for production and application purposes (e.g., for repairing or repainting a vehicle body). The output 112 can be presented on a display element, printed in a readable format, generated as audio content, or otherwise presented to a user 114 of the system 100.

[0038] Figure 2 is a simplified block diagram illustrating a method 200 for evaluating and matching the color and appearance of a coating according to an exemplary embodiment. This description assumes that at least one coating sample database 108 (e.g., reference Figure 1 The coating sample database 108 is populated with relevant data associated with a plurality of coating samples. To this end, coating sample images 202 are captured, processed, and analyzed in an appropriate manner. As described above, the captured images 202 are preferably HDR digital images captured from a plurality of different measurement angles. These captured images 202 are processed to obtain corresponding data, attributes, and / or feature descriptors (herein referred to as colored flicker characteristic data 204) that define or otherwise specify the color flicker characteristics of the various coating samples. The colored flicker characteristic data 204, the coating sample images 202, and other characterization or identification data are retained in the coating sample database 108 to allow the method 200 to compare previously characterized coating samples with coating samples (which do not require prior characterization).

[0039] A coating sample image 206 of the coating sample to be color-flicker matched is captured. As described above, the captured image 206 is preferably an HDR digital image captured from a plurality of different measurement angles. The captured coating sample image 206 is processed and analyzed to obtain corresponding color-flicker characteristic data 208, which characterizes the color-flicker and appearance of the particular coating sample. Notably, the processing and analysis performed on the coating sample image 206 is consistent with the processing and analysis performed on the different coating sample images 202, and the color-flicker characteristic data 208 characterizes, defines, or describes the coating sample in the same manner as the color-flicker characteristic data 204 for the coating sample. This allows the method 200 to compare the color-flicker appearance of the coating sample with one or more previously characterized coating samples.

[0040] In certain embodiments, at least certain aspects of the colored flicker signature data 208 (obtained from the coating sample image 206) can be compared to at least certain aspects of the colored flicker signature data 204 (obtained from the coating sample image 202). For example, the colored flicker signature data can include, but is not limited to: flicker intensity values ​​or measurements; flicker texture values ​​or measurements; visual texture values ​​or measurements; colored flicker visual scale values ​​or measurements; and pixel-based flicker point color attribute data (colored flicker gamut) or measurements. These items are described in greater detail below. The colored flicker signature data can be obtained for any set of colored flicker measurement geometries and for any number of coating samples / samples.

[0041] Method 200 may employ at least one suitable comparison metric, algorithm, or logic depending on the specific implementation and application. Figure 2The comparison function is depicted as being implemented or executed by comparison engine 210. In practice, comparison engine 210 may combine and utilize various colored flicker indicators, conventional color indicators, and other comparison indicators as needed to support specific implementations of method 200.

[0042] The comparison results can be used by appropriate search logic 212 to retrieve information corresponding to one or more matching coating samples. For example, the search logic 212 can retrieve (e.g., from the coating sample database 108 and / or from another database) any of the following (but not limited to): an identifier of the candidate coating sample; descriptive data of the candidate coating sample; color flash or appearance data of the candidate coating sample; a production recipe associated with the manufacture or mixing of the candidate coating sample. In this regard, the production recipe of the well-matched coating sample can be used to mix a coating composition to be applied to a target surface of interest (e.g., a painted surface of a vehicle).

[0043] According to certain embodiments, Figure 1 The system 100 depicted in FIG. 1 may be implemented using at least one computer-based or processor-based device, system, or piece of equipment. Figure 2 One or more of the items depicted in the can be implemented or performed using at least one computer-based or processor-based device, system or piece of equipment. In this regard, Figure 3 is a simplified block diagram of one exemplary embodiment of a computer-based device 300 that may be used to implement certain devices, systems, or components described herein.

[0044] The apparatus 300 generally includes, but is not limited to, at least one processor 302; at least one memory storage device, storage medium, or storage element 304; a display 306; at least one communication (network) interface 308; and input and output (I / O) devices 310, such as an input interface, one or more output devices, one or more human-machine interface elements, etc. In practice, the apparatus 300 may include other components, elements, and functions that may be conventional or not related to the specific applications and methods described herein.

[0045] The processor 302 can be, for example, a central processing unit (CPU), a field programmable gate array (FPGA), a microcontroller, an application specific integrated circuit (ASIC), or any other logic device or combination thereof. One or more storage elements 304 are communicatively coupled to the at least one processor 302 and can be implemented using any combination of volatile and non-volatile memory. The storage element 304 has non-temporary processor-readable and processor-executable instructions (program code) 312 stored thereon, wherein the instructions 312 can be configured to be executed by the at least one processor 302 as needed. When executed by the at least one processor 302, the instructions 312 cause the at least one processor 302 to perform the relevant tasks, processes, and operations defined by the instructions 312. Of course, the storage element 304 can also include instructions associated with the file system of the host device 300 and instructions associated with other applications or programs. In addition, the storage element 304 can be used as a data storage unit for the host device 300. For example, the storage element 304 can provide storage 314 for image data, color attribute information, sensor data, measurement results, system settings or configuration data, etc.

[0046] Display 306, if deployed with a particular embodiment of device 300, may be integral with device 300 or communicatively coupled to device 300 as a peripheral or accessory component. The shape, size, resolution, and technology of display 306 will be appropriate to the particular implementation of device 300. Display 306 may be implemented as a monitor, touch screen, or other conventional electronic display capable of graphically presenting data and / or information provided by device 300.

[0047] The communication interface 308 represents the hardware, software, and processing logic that ensures that the device 300 can support data communications with other devices. In practice, the communication interface 308 can be appropriately configured to support wireless and / or wired data communication protocols suitable for a particular embodiment. For example, the communication interface 308 can be designed to support cellular communication protocols, short-range wireless protocols (such as Bluetooth communication protocols), and / or WLAN protocols. As another example, if the device 300 is a desktop or laptop computer, the communication interface can be designed to support Bluetooth communication protocols, WLAN protocols, and LAN communication protocols (e.g., Ethernet).

[0048] I / O devices 310 ensure that users of device 300 can interact with device 300 as needed. In practice, I / O devices 310 may include, but are not limited to: an input interface for receiving data for processing by device 300; a speaker, audio transducer, or other audio feedback component; a tactile feedback device; a microphone; a mouse or other pointing device; a touchscreen or touchpad device; a keyboard; a joystick; a biometric sensor or reader (such as a fingerprint reader, a retinal or iris scanner, a palm print or vein reader, etc.); a camera; a lidar sensor; or any conventional peripheral device. In this case, touchscreen display 306 may be categorized as I / O device 310. Furthermore, touchscreen display 306 may be incorporated into or controlled to function as a fingerprint or palm print scanner. Tactile feedback devices may be controlled to generate variable amounts of tactile or physical feedback, such as vibration, force, a sense of impact or collision, detectable motion, etc. Tactile feedback devices and related control schemes are well known and therefore not described in detail here.

[0049] Figure 4 is a flowchart illustrating a method 400 that may be performed by a computing system of the type described herein, Figure 5 is a block data flow diagram of method 500, which corresponds to processing consistent with an exemplary iteration of method 400. Method 400 represents an exemplary embodiment of a method for visually scaling the colored, shimmering appearance of a coating containing at least one effect pigment type. The various tasks performed in connection with the disclosed process may be performed by software, hardware, firmware, or any combination thereof. For illustrative purposes, the description of the illustrated process may be combined with Figure 1-3 Reference is made to the elements mentioned above. It should be understood that the embodiments of the illustrated processes may include any number of additional or alternative tasks, that the tasks shown in the figures need not be performed in the order shown, and that the illustrated processes may be incorporated into more comprehensive programs or processes having additional functionality not described in detail herein. Furthermore, one or more of the tasks shown in the figures may be omitted from the embodiments of the illustrated processes as long as the intended overall functionality remains intact.

[0050] refer to Figure 4 and Figure 5, when it is necessary to match the color / appearance of a colored scintillating coating sample (e.g., a coating that has been applied to a vehicle surface) with one or more candidate colored scintillating coating samples, method 400 can be performed so that matching candidate coating samples can be identified, mixed, and utilized. For example, a matching candidate can be manufactured for application to a repaired surface of a vehicle. The embodiment shown in 400 of the method begins with receiving a digital image of a coating sample to be colored scintillating matched (task 402). As described above, any appropriately configured digital image capture system can be used to capture the digital image, and the digital image is preferably an HDR digital image generated by combining or merging image data obtained from multiple exposure devices. The initial digital image is represented by captured color image data formatted according to a first color model. In certain non-limiting embodiments, the first color model is an RGB color model, which can be the native format used by the digital image capture system. Any multi-color segment or multi-spectral camera is applicable. In this regard, Figure 5 The received digital image 502 is depicted in RGB format, where (i, j) notation represents pixel locations of the image.

[0051] According to the illustrated embodiment, method 400 continues by converting or transforming the captured color image data into corresponding relative color image data (task 404). The resulting relative color image data is formatted according to a relative tristimulus color model, both based on an RGB2XYZ transform, or based on any multi-color segment or multispectral to XYZ transform. As a non-limiting example, the relative color image data can be formatted according to an XYZ color model that models standard or normal human color vision at the human sensory level (e.g., human retinal response). To this end, Figure 5 Relative color image data 504 is depicted, obtained by subjecting the received digital image 502 to an appropriate color model conversion routine 506, either based on an RGB2XYZ transform, or based on any multi-color segment or multispectral to XYZ transform. Method 400 continues by applying at least one scene photometric parameter to the relative color image data to obtain scene-adjusted color image data (task 406). The resulting scene-adjusted color image data is formatted according to an absolute tristimulus color model, such as an XYZ color model. To this end, Figure 5 Scene adjusted color image data 508 is depicted, obtained by performing an appropriate scene photometric adjustment routine 510 on the relative color image data 504 .

[0052] The systems and methods described herein utilize task 406 and scene photometry adjustment routine 510 to account for realistic lighting conditions and viewing environments (e.g., real-world viewing conditions in a typical vehicle repair shop where actual coating samples to be matched are located and / or where matching candidate coating samples are to be applied to vehicles). These realistic conditions are believed to better simulate or predict the human visual response when comparing a coating sample to a coating sample to be color-flash matched.

[0053] In certain embodiments, task 406 applies light intensity values ​​and / or illuminance levels (measured in lux) as scene photometric parameters, where the applied light intensity values ​​and / or illuminance levels are associated with the typical viewing environment of the coating sample to be matched. In certain embodiments, task 406 applies lamp type (e.g., spectral power distribution, color temperature) as scene photometric parameters, where the applied lamp type is associated with the typical viewing environment of the coating sample to be matched. For example, portable white LED lamps with varying light intensities (illuminance levels) and different color temperatures (bluish-white vs. yellowish-white) are commonly used under refinishing field conditions. All of this information is typically measured by a dedicated spectrophotometer (remote spectroradiometer). The complete visible light spectrum (wavelength by wavelength) with its relative spectral intensities is called the spectral power distribution (SPD) or lamp spectrum. Furthermore, bluish-white LED lamps, or additional xenon lamps (such as those found in high-quality automotive headlights), are excellent simulators of natural sunlight (D65 in CIE terminology). According to certain exemplary embodiments, the colored flash model described herein is primarily based on 5000 lux xenon lamps and blue-white LED lamps under patch lighting conditions (portable lamps and directional lighting boxes) to simulate outdoor conditions typically encountered when comparing vehicle bodies.

[0054] The specific values, amounts, or levels used during task 406 and the scene photometry adjustment routine 510 can be determined by spectroradiometrically measuring and recording any number of real light sources and viewing environments, thereby enabling evaluation and characterization of one or more representative evaluation environments. The characterized environments with associated scene photometry values, amounts, or levels can be saved for subsequent use. Alternatively or additionally, the specific values, amounts, or levels used during task 406 and the scene photometry adjustment routine 510 can be variable, dynamically determined in real time or substantially in real time, user-configurable, and the like.

[0055] According to the depicted embodiment, the scene-adjusted color image data is processed in an appropriate manner to identify, separate, or otherwise distinguish between pixel data constituting scintillation points and pixel data constituting background (task 408). Both the pixel data constituting scintillation points and the pixel data constituting background are formatted according to the absolute tristimulus color model. The processing at task 408 identifies pixels that exhibit relatively high luminance levels (in cd / m²). 2 ) and pixels or pixel groups that exhibit relatively low brightness levels. Higher brightness levels indicate flicker points (associated with flakes that appear as disorganized orientations of micromirrors for this viewing flicker geometry), while lower brightness levels indicate non-flicker or background points. Method 400 can use brightness thresholds to define which pixels in the scene-adjusted color image data correspond to flicker point pixel data and which pixels correspond to background pixel data. Figure 5 The scintillation point pixel data 514 and the background pixel data 516 are displayed as separate blocks.

[0056] According to certain embodiments, method 400 is continued by converting or transforming the pixel data constituting the scintillation points into pixel-specific scintillation point data, and converting or transforming the pixel data constituting the background into pixel-specific background data (task 410). The conversion at task 410 produces color image data formatted according to an advanced color appearance model that models human color perception. In other words, task 410 converts the color model of the pixel-based color image data from a human retina model to a human brain or visual cortex model, thereby improving the high contrast of the scintillation points on the darker colored background originally encoded in the RGB captured image. Therefore, method 400 uses the absolute XYZ model as a bridge to convert the captured color image data (in RGB format) into pixel-specific scintillation point data and pixel-specific background data (in human color perception format). As a non-limiting example, the advanced color appearance model can be a JMH model, where J is a numerical value representing lightness (brightness relative to white), M is a numerical value representing color saturation or color, and H is a numerical value representing hue.

[0057] Figure 5 Pixel-specific scintillation point data 520 is shown, which is obtained by subjecting the constituent scintillation point pixel data 514 to an appropriate color appearance model conversion routine 522. Similarly, pixel-specific background data 524 is obtained by subjecting the constituent background pixel data 516 to a color appearance model conversion routine 522. Figure 5 As indicated, for this particular embodiment, the pixel-specific scintillation point data 520 and the pixel-specific background data 524 are both defined according to the JMH high-level color appearance model.

[0058] Method 400 can be continued by performing an appropriate segmentation routine on the pixel-specific scintillation point data and the pixel-specific background data to identify scintillation point pixels that exhibit visually distinguishable scintillation effects and meet a color difference (ΔE) threshold specified according to an advanced color appearance model (task 412). According to certain embodiments, task 412 performs a ΔE-JMH image segmentation routine to identify high color contrast scintillation pixels. The segmentation performed at task 412 is based on the color difference between the scintillation point pixels and the darker colored background in the image being analyzed. Figure 5 A ΔE-JMH image segmentation routine 528 is depicted that operates on pixel-specific scintillation point data 520 and pixel-specific background data 524 .

[0059] According to certain embodiments, the segmentation performed at task 412 processes the pixel-specific scintillation point data 520 and the pixel-specific background data 524 to obtain a pixel-specific ΔE-JMH value (color difference data) corresponding to the image being analyzed. For example, the ΔE-JMH value for a given pixel ranges from 0 to 100, where a value of 0 represents a background pixel with no detectable color difference and a value of 100 represents a scintillation pixel with very high and distinguishable color difference. In practice, the upper limit need not be 100, and in certain embodiments, the value can be less than or greater than 100, and will always depend on the effect coating formulation. A typical image of a colored scintillation coating will exhibit a large number of background pixels (ΔE-JMH values ​​close to 0) with randomly distributed scintillation points or areas (ΔE-JMH values ​​above a certain threshold that defines whether a pixel is a colored scintillation pixel or a background pixel). The ΔE-JMH threshold used for this determination may vary from implementation to implementation and is not necessarily fixed for any given embodiment, but is always based on the statistical properties of the high-contrast scintillation point (intensity) histogram, which can be easily fitted by a statistical distribution like Gaussian, Gamma, etc., where the mean, median, standard deviation, quartiles, percentiles, etc. can be used. According to one non-limiting example, based on the gamma statistics and the 95% quantile, the ΔE-JMH threshold is 11.5, so ΔE-JMH values ​​above 11.5 are assumed to indicate high-contrast colored scintillation pixels encoded in the long right tail of the scintillation point histogram; ΔE-JMH values ​​less than or equal to 11.5 are therefore assumed to indicate background pixels, even pixels corresponding to orientationally disordered flakes that do not appear as micromirrors under this observation scintillation geometry.

[0060] It is noteworthy that the colored glitter region may include many high color contrast pixels surrounded by many low color contrast pixels, which in turn are surrounded by many low color contrast pixels. The segmentation routine performed at task 412 identifies and locates the colored glitter regions, points, and corresponding pixels to distinguish them from background pixels. Method 400 assumes that the spatial pattern of the colored glitter regions, points, and pixels represents the colored glitter appearance of the overall coating as perceived by a human observer at a close distance (e.g., 0.5 meters).

[0061] In some embodiments, the segmentation performed at task 412 counts the colored glint regions in a recursive manner without repetition, starting with the region with the highest peak ΔE-JMH value, calculating the average around a mask (e.g., a 25 x 25 pixel mask) centered at its peak, and finally removing this first identified region (a diffraction-limited segmentation unit, which depends on the actual viewing distance of a human observer observing the sample / specimen, e.g., approximately 0.5 meters). The segmentation routine is then repeated to search for the next colored glint region with the highest peak ΔE-JMH value. This procedure is repeated to ensure sufficient coverage of the entire image being analyzed, and preferably, this process is repeated for all pixel-based image data to identify and locate all highest color contrast regions.

[0062] Look again Figure 4 and Figure 5 , the illustrated embodiment of method 400 is continued by determining a sparkle intensity value (S) and a sparkle texture value (Td) for the coating sample to be color sparkle matched (task 414). For the example described, S is a unitless value indicating high color contrast, and Td is a value indicating the physical density of the sparkle dots (in this example, Td has units of mm). -2 or per mm 2 The values ​​of S and Td are determined by the pixel-specific scintillation point data and the pixel-specific background data. More specifically, the values ​​of S and Td are determined in response to the results of the ΔE-JMH segmentation routine 528. In this regard, Figure 5 The flicker intensity values ​​532 and flicker texture values ​​534 obtained as a result of the ΔE-JMH segmentation routine 528 are depicted.

[0063] Td is defined as the physical density of the scintillation points present in all images based on a known single pixel area and represents the entire coating. Therefore, after the colored scintillation points obtained after the ΔE-JMH segmentation are completed are counted, Td can be easily calculated. In certain embodiments, S is calculated as a function of the ΔE-JMH value. More specifically, S is calculated by the ΔE-JMH values ​​of the segmented image data (i.e., pixels that have been declared to have colored scintillation points with high color contrast). For this specific non-limiting example, S is calculated as the average value of the ΔE-JMH values ​​of the segmentation, but always minus a threshold value (e.g., 11.5). Therefore, a coating with a strong scintillation effect or very high colored scintillation contrast will have a relatively high S value, while a coating with a weak scintillation effect or lower colored scintillation contrast will have a relatively low S value.

[0064] It is noteworthy that when the light intensity and / or lamp type of the observation environment changes, the Td and S values ​​of the colored scintillating coating being analyzed will also change. Method 400 is able to take into account such environmental changes and accurately calculate the values ​​of Td and S in a responsive manner. In this way, the disclosed colored scintillating method is a lighting adaptive method. When the light intensity and / or lamp type changes, the captured RGB image will also change, which in turn will change the JMH image data information of the background and scintillating points. Although the spatial pattern (the distribution of scintillating points in a non-scintillating background, or texture) is image-fixed, the scintillating point intensity histogram after segmentation can reflect some changes that affect the values ​​of S and Td.

[0065] Look again Figure 4 and Figure 5 , the illustrated embodiment of method 400 continues by calculating a visual texture value (T) for the coating sample to be color-flash matched (task 416 ). Figure 5 A visual texture value 536 is shown that is derived from the glint texture value 534. For the example, T is calculated as a function of Td based on the following expression:

[0066]

[0067] In Expression 1.0, α and η are constants calculated using instrumental and visual data using supervised machine learning methods (nonlinear regression). In Expression 1.0, α is always a positive number, such as 3 or 11. Furthermore, the absolute value of η is less than 1, and its final sign (positive or negative) is adapted to the lighting conditions of the visual experiment using the colored flicker vision scale.

[0068] In practice, the original (imaging) quantities Td and S cannot be used directly to predict the visual texture of colored flicker. Td, as the physical density of the flickering points, cannot be easily and accurately estimated by human observers. On the other hand, human observers can estimate some values ​​associated with the visual density of the flickering points (such as T values). Therefore, for the appearance of colored flicker, any observer who has received basic training should be able to distinguish samples with similar S values ​​and high or low visual density (T) of flickering points. Although method 400 obtains computer vision quantities (Td and S), these values ​​are not directly related or proportional to the perceived T and S values ​​encoded by the human brain. Therefore, the visual texture value (T) is a more effective and accurate value for visual scaling and comparison of coating samples. The above expression 1.0 about T utilizes visual experiments and is a good predictor of the visual density of flickering points. Visual experiments that also cover the colored flicker visual scaling of converting Td to T can be designed and executed psychophysically using colorful colored flicker samples under directional lighting conditions that replicate the target field conditions according to methods such as multidimensional scaling, maximum likelihood difference scaling, etc.

[0069] Replay Figure 4 , the illustrated embodiment of method 400 is continued by calculating a colored flicker visual scale value for the coating sample to be colored flicker matched (task 418). The calculated colored flicker visual scale value can be stored and / or used as needed (task 420). One or more other colored flicker characteristics or parameters (e.g., calculated S, Td, and T values ​​and colored flicker gamut) and associated color image data can also be stored and / or used as needed or required for a particular application. For example, the calculated CSv, T, and S values ​​for the coating sample can be used in a colored flicker matching and retrieval process to search for a colored flicker coating sample that matches the colored flicker characteristics of the coating sample.

[0070] like Figure 5 As schematically depicted, a colored flicker visual scale value (CSv) 540 is calculated as a function of the flicker intensity value 532 and the visual texture value 536. Thus, the colored flicker visual scale value 540 is calculated based on the pixel-specific flicker point data 520 and the pixel-specific background data 524. The resulting colored flicker visual scale value 540 can be expressed as a unitless numerical value indicative of the colored flicker characteristics of the coating sample to be colored flicker matched. According to certain non-limiting embodiments, typical values ​​of CSv can range from about 1.0 to about 20, where CSv=0 indicates the absence of a colored flicker appearance, typically representing a coating without effect pigments.

[0071] According to certain embodiments, the colored flicker visual scale value 540 is calculated based on the colored flicker characteristics of the identified flicker pixels (i.e., based on the flicker points obtained after performing the ΔE-JMH segmentation routine 528). For this particular example, the colored flicker visual scale value 540 is a function of the visual texture value (T) and the flicker intensity value (S):

[0072]

[0073] In expression 2.0, β and offset are constants that are calculated using instrumental and visual data using supervised machine learning methods (nonlinear regression). For this particular application, the values ​​of β and offset in expression 2.0 can be any positive number, for example, β is 0.5 and offset is 2.7 (other values ​​can be used depending on the specific embodiment and application). These constants are determined experimentally based on human observation and comparison of colored glitter samples, so higher CSv values ​​correspond to coating samples that are observed to have a higher amount of colored glitter effect. According to certain non-limiting embodiments, β=0.5 and offset=2.7.

[0074] The Colored Flicker Visual Scale Value (CSv) represents a simple numerical scale—based on the relationship between physical texture values ​​(Td), visual texture values ​​(T), and flicker intensity values ​​(S)—that can be used as a colored flicker metric when comparing different colored flicker coatings. By applying supervised machine learning (nonlinear regression) between visual experiment data and system-generated data (Td, raw S) and exploring different nonlinear functions (polynomials, etc.), the most effective nonlinear function was determined to be a square root-based function involving a scaling factor (β) and an offset value, as described above. Because Td and raw S are determined under known lighting conditions (light intensity and lamp type), any changes in these conditions will affect the values ​​of Td and S. Therefore, visual experiments can be repeated, and the same supervised machine learning method can be used to adjust the various parameters (α, η, β, offset) to maintain an accurate correlation between visual perception and system output, which in turn provides excellent colored flicker predictability.

[0075] Figure 6 is a graph including graphs of different colored flicker vision scale values. Figure 6 The graphs, associated S and T values, and different colored flicker visual scale values ​​shown in FIG are provided only as a few examples to help describe and understand the colored flicker visual scale value (CSv) and its relationship to the flicker intensity value (S) and the visual texture value (T). In practice, the characteristics, properties, and visualization of a given CSv graph do not necessarily have to be as specific as the ones shown in FIG. Figure 6 Although not important for the purposes of this specification, Figure 6The graph in shows the CSv values ​​of different coatings observed under the following lighting conditions: illumination angle of 15 degrees; viewing angle of 0 degrees; xenon lamp; 5,000 lux.

[0076] exist Figure 6 , the vertical axis represents S and the horizontal axis represents T. Therefore, each CSv graph shows how specific values ​​of S and T affect the CSv value. Figure 6 Eight different CSv graphs are depicted and include a legend indicating the CSv for each graph. More specifically: graph 602 represents CSv = 1; graph 604 represents CSv = 2; graph 606 represents CSv = 4; graph 608 represents CSv = 6; graph 610 represents CSv = 8; graph 612 represents CSv = 10; graph 614 represents CSv = 12; graph 616 represents CSv = 14; and graph 618 represents CSv = 16. Other CSv values ​​will be associated with non-intersecting graphs located between the two illustrated graphs.

[0077] As explained in more detail below, the color flicker visual scale value 540 can be utilized when initially testing or filtering candidate coating samples for color flicker matching. In this regard, the color flicker visual scale value 540 can be used as a component in determining how well a candidate coating sample matches a coating sample to be color flicker matched. To this end, the coating sample database 108 (see Figure 1 ) contains the colored flicker visual scale values ​​and other characteristic information that has been calculated for various candidate coating samples (e.g., captured RGB color image data and any converted / transformed representations of that color image data, corresponding T, S, and Td values, colored flicker gamut, etc.). The data maintained in the coating sample database 108 can be accessed as needed for consideration by the comparison engine 210.

[0078] Figure 7 is a graph including different colored glint visual scale values ​​and labels corresponding to coating samples and candidate coating samples. More specifically, Figure 7 Depicted also Figure 6 606, 608, 610, 612, 614, and portions of 616 are shown in FIG. Figure 7 Also included are: indicia 630 indicating the S, T, and CSv values ​​of the coating sample to be color flash matched; indicia 632 indicating the S, T, and CSv values ​​of the candidate coating sample; and area 634 representing exemplary visual matching tolerances that may be used when comparing the candidate coating sample to the coating sample to be color flash matched.

[0079] According to certain embodiments, region 634 is elliptical and centered on the coating sample's mark 630, and its size defines a specific colored flicker threshold value used to determine how closely the colored flicker characteristics of the candidate coating sample match the colored flicker characteristics of the coating sample. The elliptical shape of region 634 takes into account the predictable graph shape and profile (e.g., Figure 6 and Figure 7 It will be appreciated that alternative embodiments may utilize non-elliptical tolerance zones and adjust their shape as needed to meet the needs and requirements of a particular application. Figure 7 The candidate coating sample's mark 632 is shown as being outside of region 634. Therefore, the candidate coating sample corresponding to mark 632 is determined to be a poor match with the coating sample represented by mark 630 (having a similar visual texture T to the sample, but a significantly different intensity S).

[0080] Figure 8 is a flow chart illustrating a method 700 that can be executed by a computing system of the type described herein. Method 700 represents an exemplary embodiment of a method for matching the colored sparkle appearance of a coating containing at least one effect pigment type. The following description of method 700 assumes that the following characteristic values ​​have been calculated for the coating sample to be matched and at least one candidate coating sample: CSv, T, S, and colored sparkle gamut. According to the exemplary embodiments described herein, method 400 is executed to obtain these characteristic values.

[0081] The illustrated embodiment of method 700 begins by retrieving or accessing the colored flicker visual scale values ​​(CSv) of the coating sample to be matched for colored flicker and the candidate coating sample. As described above, the CSv of the coating sample indicates the colored flicker characteristics of the coating sample, while the CSv of the candidate coating sample indicates the colored flicker characteristics of the candidate coating sample. Continuing with method 700, the CSv of the coating sample is compared with the CSv of the candidate coating sample and a visual scale difference is calculated (task 702). For this example, the visual scale difference value (ΔCSv) is the absolute value of the difference between the two colored flicker visual scale values. The visual scale difference is compared with a visual scale threshold TH1 (query task 704). The colored flicker visual scale threshold is used to determine whether the candidate coating sample and the coating sample to be matched for colored flicker are poorly matched. More specifically, when the visual scale difference is greater than the visual scale threshold (i.e., when ΔCSv>TH1), method 700 determines that the candidate coating sample and the coating sample are poorly matched. In this manner, method 700 makes a preliminary determination of the degree of match between the candidate coating sample and the coating sample, based on the two CSv values. The definition of TH1 may vary from implementation to implementation depending on the specific application. According to non-limiting examples contemplated herein, TH1 may be in the range of approximately 2.0 to 4.0, preferably approximately 3.0.

[0082] The vertical CSv tolerance is the CSv tolerance, which is about 2.0 from the visual experiment of the colored flicker vision scale. That is, Figure 6 Parallel CSv curves in the graph (4 vs. 6, etc.) indicate regions where the appearance of colored flicker is clearly discernible. For this reason, a colored flicker visual scale difference of >3 or higher indicates poor color flicker matching. However, due to the square root of the CSv equation, different effect coatings with different (T,S) pairs can share the same CSv but visually exhibit ΔT and / or ΔS values ​​above the colored flicker tolerance, thus making these coatings unmatched for color flicker even if the colored flicker visual scale difference is close to zero. Therefore, the colored flicker visual scale difference is a first screening step, acting as a "coarse filter," rather than the most relevant (sharp or fine) filter for the final color flicker matching screen.

[0083] If ΔCSv>TH1 ("yes" branch of query task 704), then method 700 determines that the candidate coating sample being analyzed does not match the coating sample. At this point, method 700 can exit or continue to examine the next candidate coating sample (task 706). Figure 8 Task 706 leading back to task 702 is depicted so that the initial test based on the colored flicker visual scale value can be repeated for the next candidate coating sample. If the difference in visual scale value is not greater than TH1 (the "no" branch of query task 704), method 700 will continue to perform at least one additional test or check to determine the degree of match between the candidate coating sample and the colored flicker characteristic of the coating sample. In this regard, the "no" indication at query task 704 indicates that the currently selected candidate coating sample has passed the initial test for matching the colored flicker of the coating sample. Although the matching criteria associated with query task 704 can be used to obtain a good approximation of a colored flicker match, method 700 includes additional tests (described below) that improve the accuracy of the matching decision.

[0084] According to certain embodiments, the next test / check performed by method 700 is based on the previously calculated flicker intensity (S) and visual texture (T) values. To this end, method 700 calculates the visual texture difference (ΔT) between the visual texture value of the candidate coating sample and the visual texture value of the coating sample to be matched with colored flicker (task 708). For this example, the visual texture difference (ΔT) is the difference between these two visual texture values. Similarly, method 700 calculates the flicker intensity difference (ΔS) between the flicker intensity value of the candidate coating sample and the flicker intensity value of the coating sample to be matched with colored flicker (task 710). For this example, the flicker intensity difference (ΔS) is the difference between these two flicker intensity values. The signs (positive or negative) of ΔT and ΔS can be used to detect local deviations in visual texture and intensity, respectively, but the amplitude (absolute value) is more relevant to the final colored flicker difference (ΔCS). The signs of ΔT and ΔS can be used to adjust the color formula of the sample to match it with the colored flicker difference (ΔCS) of the coating sample.

[0085] Method 700 continues by calculating the color sparkle difference (ΔCS) between the candidate coating sample and the coating sample to be color sparkle matched (task 712). Figure 8 As shown, ΔCS is calculated as a function of ΔT and ΔS, which represents the difference in the spatial pattern of colored flashes between the candidate coating sample and the coated sample. ΔCS also indicates the difference in macrotexture between the candidate coating sample and the coated sample.

[0086] According to certain embodiments, ΔCS is initially calculated in an elliptical form that takes into account the elliptical tolerance region (as described above with reference to Figure 7 Specifically, ΔCS is calculated according to the following expression:

[0087]

[0088] In Expression 3.0, T k Is the colored flash tolerance: is the tangential colored flash tolerance, T CSv is the vertical colored flash tolerance. ΔT is a parameter associated with the inclination of the tolerance ellipse (between 0 and 90 degrees). The calculation of these three parameters is always based on the knowledge of the (T, S) pair of the coating sample (standard part, vehicle component, etc.). x and ΔS y It is calculated from the instrument's ΔT, ΔS, and the rotation matrix obtained from the tangent inclination angle of the coating sample (standard, etc.) at the (T, S) point.

[0089] To simplify processing and analysis, the elliptical form of ΔCS can be converted to a circular form. To this end, ΔCS can also be calculated according to the following expression (circular form):

[0090]

[0091] Each bracket from Expression 3.0 is renamed in Expression 4.0, except for the exponent = 2. This represents the conversion of the colored flicker difference ΔCS from an elliptical form to a circular form with the same numerical results. As mentioned above, since the equation for CSv is based on square roots, the elliptical form of the colored flicker difference is converted to the circular form of the colored flicker difference with the same numerical results after applying the corresponding colored flicker tolerances: tangential CSv tolerance, vertical CSv tolerance. A visual experiment combining instrumental differences (ΔT, ΔS) with visual data (OK or Acceptable, NOK or Reject) was designed and performed under the same lighting conditions as the colored flicker vision scale, but using a different set of colored flicker samples, to determine (Tangential Colored Flash Tolerance) and T CSv (Vertical Colored Flicker Tolerance). For this particular application, the expression in 3.0 and T CSv The value of can be any positive number, for example, is 2.8, T CSv is 1.8 (other values ​​may be used, depending on the specific embodiment and application).

[0092] The circular form of ΔCS is more intuitive and practical because it allows the use of a circular tolerance zone centered on the point corresponding to the coating sample to be color-flash matched. According to the exemplary embodiment described herein, the radius of the circular tolerance zone is (approximately 1.41). When analyzing the calculated ΔCS value, a value of 1.41 can be used as a threshold.

[0093] Reference again Figure 8 Continuing with the illustrated embodiment of method 700, the calculated ΔCS value is compared to a colored flicker threshold value (TH2) defined and maintained for the particular system (query task 714). As described above, TH2 can be specified as 1.41, or it can be any suitable value within the range of approximately 0.5 to 2.5, such as 1.5. The colored flicker threshold value is used to determine whether the candidate coating sample is a poor match with the coating sample to be colored flicker matched. In this regard, when the colored flicker difference is greater than the colored flicker threshold value (i.e., when ΔCS>TH2), method 700 determines that the candidate coating sample is a poor match with the coating sample. In this manner, method 700 makes a secondary determination of the match between the candidate coating sample and the coating sample.

[0094] If ΔCS>TH2 ("yes" branch of query task 714), then method 700 determines that the candidate coating sample being analyzed does not match the coating sample. At this point, method 700 can exit or continue to examine the next candidate coating sample (task 706). Figure 8 Task 706 is depicted, leading back to task 702, so that the initial test based on the colored sparkle visual scale value can be repeated for the next candidate coating sample. If ΔCS is not greater than TH2 (the "no" branch of query task 714), method 700 will continue to perform at least one additional test or inspection to determine the degree of match between the candidate coating sample and the near-visual texture colored sparkle characteristics of the coating sample. For example, a typical viewing distance for sparkle is approximately 0.5 meters. If the effect pigment particle size is small (12 microns), the colored sparkle becomes less visible at greater distances. At this typical viewing distance (e.g., 0.5 meters), any normal human observer can discern the presence of individual sparkle dots on a darker colored background, with the various colored sparkle dots primarily caused by pearlescent pigment types. Aluminum pigment types primarily cause a colorless sparkle appearance (or light gray sparkle dots). Pearlescent pigment types primarily cause a colored or colored sparkle appearance, wherein at a distance of 0.5 meters, visual recognition of colors with varying degrees of color or saturation, such as reddish, bluish, or greenish, is very clear.

[0095] A "no" decision at query task 714 indicates that the currently selected candidate coating sample passes the secondary test for a color flicker match with the coating sample. The primary and secondary match criteria associated with query tasks 704 and 714 can be used to obtain a good approximation of a color flicker match. Nevertheless, the illustrated embodiment of method 700 includes at least one additional test (described below) to improve the accuracy of the color flicker match determination.

[0096] According to certain embodiments, the next test / check performed by method 700 will consider the colored flicker gamuts of the coating sample and the candidate coating sample. For this particular example, the colored flicker gamut of the coating sample is associated with the JMH color image data of the coating sample, and the colored flicker gamut of the candidate coating sample is associated with the JMH color image data of the candidate coating sample. Figure 8 Schematically depicting pixel-based JMH color model attribute data 716 for a coating sample and pixel-based JMH color model attribute data 718 for a candidate coating sample. Figure 5 As described above, the JMH color model attribute data 716 , 718 correspond to the identified high color contrast sparkle points of the coating sample to be subjected to color sparkle matching and the candidate coating sample.

[0097] The depicted embodiment of method 700 calculates a minimum color difference indicator (MICD) from JMH color model attribute data 716, 718 (task 720). The MICD can be calculated based on a comparison of a colored glint palette associated with high color contrast glint points (identified for the coating sample and the candidate coating sample). In this regard, Figure 9 A method for calculating MICD from pixel-based JMH color model attributes of coating samples and candidate coating samples is illustrated. Figure 9 The left side of FIG depicts a matrix 802 including JMH color model attribute values ​​for candidate coating samples. Figure 9 The right side of is depicted a matrix 804 comprising JMH color model attribute values ​​for the coating samples to be colored flicker matched. Matrix 802 includes a number (N) of rows corresponding to the N identified high color contrast flicker points of the candidate coating sample; each row contains three columns (J, M, and h values, with h in degrees). Similarly, matrix 804 includes a number (P) of rows corresponding to the P identified high color contrast flicker points of the coating sample; each row contains three columns (J, M, and h values, with h in degrees). Although N and P are typically different values, they can also be the same in some cases. Each row in matrix 802 is identified by an index i (ranging from 1 to N), and each row in matrix 804 is identified by an index k (ranging from 1 to P).

[0098] The MICD calculation is based on many individual color difference calculations. More specifically, each identified sparkle point of the candidate coating sample is individually compared to each identified sparkle point of the coating sample to obtain a corresponding color difference value (ΔE ik ), thereby obtaining N color difference values. The minimum value among the N color difference values ​​of the candidate coating sample's scintillation point is selected, identified, or recorded. This routine is repeated for each identified scintillation point of the candidate coating sample, thereby recording the N minimum color difference values ​​associated with the candidate coating sample. A similar procedure is performed to compare each identified scintillation point of the coating sample with each identified pixel of the candidate coating sample, thereby obtaining P color difference values ​​(ΔE) corresponding to the corresponding row in matrix 804. ki ). Select, identify or record the minimum of the P color difference values ​​of the scintillation point of the coating sample. Repeat this routine for each identified scintillation point of the coating sample, thereby recording the P minimum color difference values ​​associated with the coating sample to be colored scintillation matched.

[0099] Figure 9 The method of obtaining the minimum color difference value is shown. The solid arrow 808 indicates that the N color difference values ​​(ΔE ik ). Determine the minimum value of these N color difference values ​​for subsequent use - Figure 9The minimum value is marked as "min1" on the left side of . An equivalent routine is performed to obtain the minimum color difference value for each flash point (each row of matrix 802). Figure 9 Mark N minimum values ​​on the left side of 1-N ). Dashed arrow 810 indicates the calculation of the P color difference values ​​(ΔE) of the last pixel (where k=P) in matrix 804. ki ). Follow the same method to Figure 9 Mark P minimum values ​​on the right side of 1-P ).

[0100] According to the exemplary embodiment presented herein, the color difference value (when comparing the sparkle point of the candidate coating sample to the sparkle point of the coating sample to be color-sparkle matched) is calculated by the following expression:

[0101]

[0102] In these expressions, the values ​​of J, M, and h (in degrees) correspond to the pixel-specific JMH color model attributes appearing in matrices 802 and 804. Expression 6.0 represents the conversion of raw hue angle data into corresponding visually relevant hue data, which in turn can be used to calculate color difference values. Similarly, color difference values ​​(when comparing the sparkle point of a coating sample to the sparkle point of a candidate coating sample) are calculated using the following expression:

[0103]

[0104] Look again Figure 9 , the first value (labeled as M1) is taken as ΔE ik Minimum value (i.e. min1 to min N ) is obtained as a function of , and the second value (labeled as M2) is obtained as ΔE ki Minimum value (i.e. min1 to min P According to certain embodiments, M1 is defined as the minimum value min1 to min N The average value of M2 is defined as the minimum value from min1 to min PTherefore, when the colored sparkle point of the candidate coating sample is compared with the colored sparkle point of the coating sample to be matched, M1 provides a good approximation of the overall color difference, while when the colored sparkle point of the coating sample is compared with the colored sparkle point of the candidate coating sample, M2 provides a good approximation of the overall color difference. For the exemplary embodiment given here, the MICD is obtained as a function of M1 and M2. More specifically, the MICD is defined as the average of M1 and M2. Therefore, the MICD value represents a good characterization of the color sparkle difference between the candidate coating sample and the coating sample to be color sparkle matched. In theory, a MICD value of zero indicates a perfect color sparkle gamut match, and a higher MICD value indicates a correspondingly higher color sparkle gamut mismatch.

[0105] return Figure 8 At task 720, method 700 calculates the MICD and compares the MICD to a color difference threshold (TH3) defined and maintained for the particular system (query task 722). The color difference threshold can be set to approximately 1.5 or approximately 2.0, or can be any suitable value within the range of approximately 1.0 to 3.0. The color difference threshold is used to determine the degree of match between the candidate coating sample and the coating sample (with respect to the colored flicker color gamut characteristics). In this regard, when the MICD is greater than the color difference threshold (i.e., when MICD>TH3), method 700 determines that the candidate coating sample is a poor match with the coating sample. In this manner, method 700 makes a final determination as to the degree of match between the candidate coating sample and the coating sample.

[0106] If MICD>TH3 ("yes" branch of query task 722), method 700 determines that the candidate coating sample being analyzed does not match the coating sample. At this point, method 700 can exit or continue to check the next candidate coating sample (task 706). Figure 8 Task 706 is depicted, leading back to task 702 so that the initial test based on the colored flicker vision scale value can be repeated for the next candidate coating sample. If the MICD is not greater than TH3 (the "no" branch of query task 722), then method 700 determines or declares that the candidate coating sample matches the coating sample to be matched for colored flicker in terms of both the spatial pattern (macrotexture) and the colored flicker gamut (nearsighted texture) (task 724). In this regard, the "no" decision at query task 722 indicates that the currently selected candidate coating sample has passed the initial, secondary, and final tests for matching the colored flicker with the coating sample.

[0107] If all matching criteria are met (the "No" branch of query task 722, indicating that the candidate coating sample is a good match with the coating sample in terms of color scintillation characteristics), the method 700 can identify the candidate coating sample as a good color scintillation match (task 724) and retrieve, access, or otherwise obtain information or data associated with the candidate coating sample. For example, the method 700 can obtain certain colorimetric properties of the candidate coating sample (task 726). This information may already be stored in the coating sample database 108 (see Figure 2 ), or can be provided as needed or on demand. As another example, method 700 can retrieve a production recipe or "recipe" associated with the candidate coating sample (task 728) and provide the retrieved information as output for consideration by a user, technician, or operator of the coating application system. The production recipe may already be stored in the coating sample database 108, or can be provided as needed or on demand. In certain embodiments, the retrieved information (e.g., the production recipe) can be provided to a mixing or manufacturing system or station that mixes the coating based on the retrieved recipe.

[0108] Although Figure 8 Although not separately shown, after identifying a matching coating sample, method 700 can return to task 706. In fact, the comparison process of method 700 can be repeated any number of times until a desired number of candidate coating samples are identified as potential matches. The user can then be presented with different options, sample images of the candidate coating samples, and / or other information to ensure that the user makes an informed decision about which (if any) candidate coating sample to select.

[0109] The present invention can be further described by the following aspects.

[0110] Aspect 1. A computer-implemented method for evaluating and matching the colored shimmering appearance of a coating containing at least one effect pigment type, the method comprising:

[0111] receiving a digital image of a coating sample to be color flash matched, the digital image captured by a digital image capture system and represented by captured color image data formatted according to a first color model native to the digital image capture system;

[0112] converting the captured color image data into pixel-specific scintillation point data and pixel-specific background data, wherein the pixel-specific scintillation point data and the pixel-specific background data are formatted according to an advanced color appearance model corresponding to human color perception;

[0113] Calculating a first color flicker vision scale value of the coating sample to be color flicker matched from the pixel-specific flicker point data and the pixel-specific background data, wherein the first color flicker vision scale value indicates a color flicker characteristic of the coating sample to be color flicker matched;

[0114] retrieving a second colored flicker vision scale value for the candidate coating sample, wherein the second colored flicker vision scale value indicates a colored flicker characteristic of the candidate coating sample;

[0115] comparing the first colored flicker vision scale value to the second colored flicker vision scale value; and

[0116] A degree of match between the candidate coating sample and the coating sample to be color flash matched is determined based on the results of the comparing step.

[0117] Aspect 2. The method according to aspect 1, wherein the converting step further comprises:

[0118] The captured color image data is converted to relative color image data formatted according to a relative tristimulus color model.

[0119] Aspect 3. The method according to aspect 2, wherein the converting step further comprises:

[0120] At least one scene photometric parameter is applied to the relative color image data to obtain scene-adjusted color image data formatted according to an absolute tristimulus color model.

[0121] Aspect 4. The method of aspect 3, wherein the at least one scene photometric parameter comprises a light intensity value or illuminance level associated with an observation environment of the coating sample to be matched.

[0122] Aspect 5. The method of aspect 3, wherein the at least one scene photometric parameter comprises a light type associated with an observation environment of the coating sample to be matched.

[0123] Aspect 6. The method according to aspect 3, wherein the converting step further comprises:

[0124] The scene-adjusted color image data is processed to distinguish between constituent scintillation point pixel data and constituent background pixel data, wherein the constituent scintillation point pixel data and the constituent background pixel data are both formatted according to an absolute tristimulus color model.

[0125] Aspect 7. The method according to aspect 6, wherein the converting step further comprises:

[0126] converting the constituent scintillation point pixel data into pixel-specific scintillation point data formatted according to the advanced color appearance model; and

[0127] The constituent background pixel data is converted into pixel-specific background data formatted according to a high-level color appearance model.

[0128] Aspect 8. The method according to aspect 1, wherein:

[0129] The computing step further includes performing a segmentation routine on the pixel-specific scintillation point data and the pixel-specific background data to identify scintillation pixels exhibiting visually distinguishable scintillation effects that satisfy a color difference threshold based on a high-level color appearance model; and

[0130] A first colored flicker vision scale value is calculated from the colored flicker characteristics of the identified flicker pixels.

[0131] Aspect 9. The method according to aspect 1, wherein:

[0132] The calculating step further comprises determining a sparkle intensity value of the coating sample to be color-sparkle matched from the pixel-specific sparkle point data and the pixel-specific background data, wherein the sparkle intensity value indicates a high color contrast;

[0133] The calculating step further comprises determining a glitter texture value of the coating sample to be color-sparkle matched from the pixel-specific glitter point data and the pixel-specific background data, wherein the glitter texture value indicates a physical density of glitter points; and

[0134] The first colored flicker visual scale value is calculated from the flicker intensity value and the flicker texture value.

[0135] Aspect 10. The method according to aspect 9, wherein:

[0136] The calculating step further comprises calculating a visual texture value from the flicker texture value; and

[0137] The first colored flicker visual scale value is calculated from the flicker intensity value and the visual texture value.

[0138] Aspect 11. The method according to aspect 1, further comprising:

[0139] calculating a visual scale difference between the first colored flicker visual scale value and the second colored flicker visual scale value;

[0140] comparing the visual scale difference to a visual scale threshold; and

[0141] When the visual scale difference is greater than the visual scale threshold, it is determined that the candidate coating sample and the coating sample to be colored flash matched are poorly matched.

[0142] Aspect 12. The method according to aspect 11, further comprising:

[0143] When the visual scale difference is not greater than the visual scale threshold, calculating the color flicker difference between the candidate coating sample and the coating sample to be matched with the color flicker;

[0144] comparing the colored flicker difference to a colored flicker threshold; and

[0145] When the color flicker difference is greater than the color flicker threshold, it is determined that the candidate coating sample and the coating sample to be color flicker matched are poorly matched.

[0146] Aspect 13. The method according to aspect 12, further comprising:

[0147] determining a sparkle intensity value for a coating sample to be color sparkle matched from the pixel-specific sparkle point data and the pixel-specific background data, wherein the sparkle intensity value indicates a high color contrast;

[0148] determining a visual texture value of a coating sample to be color-sparkle matched from the pixel-specific scintillation point data and the pixel-specific background data, wherein the visual texture value indicates a visual density of the scintillation points;

[0149] calculating a sparkle intensity difference between the sparkle intensity value of the candidate coating sample and the sparkle intensity value of the coating sample to be color sparkle matched; and

[0150] calculating a visual texture difference between the visual texture value of the candidate coating sample and the visual texture value of the coating sample to be colored flash matched;

[0151] The color flicker difference or spatial pattern difference is calculated as a function of flicker intensity difference and visual texture difference.

[0152] Aspect 14. The method according to aspect 12, further comprising:

[0153] calculating a minimum color difference index based on pixel-based color model properties of the high color contrast flicker points identified for the candidate coating sample and for the coating sample to be colored flicker matched when the colored flicker difference is not greater than the colored flicker threshold;

[0154] Compare the minimum color difference index with the color difference threshold;

[0155] When the minimum color difference index is greater than the color difference threshold, determining that the candidate coating sample and the coating sample to be colored flash matched are poorly matched; and

[0156] When the minimum color difference index is not greater than the color difference threshold, it is determined that the candidate coating sample matches the coating sample to be matched in terms of color flicker.

[0157] Aspect 15. The method according to aspect 14, further comprising:

[0158] In response to determining that the candidate coating sample matches the coating sample to be color flash matched, a production recipe associated with the candidate coating sample is retrieved.

[0159] Aspect 16. The method of aspect 14, wherein the minimum color difference index is calculated based on a comparison of a colored flicker palette associated with a high color contrast flicker point.

[0160] Aspect 17. A computing system operable to evaluate and match the color and appearance of a coating containing at least one effect pigment type, the computing system comprising:

[0161] at least one processor; and

[0162] At least one non-transitory machine-readable storage medium storing instructions executable by the at least one processor, the instructions being configurable to cause the at least one processor to perform the following method, the method comprising the steps of:

[0163] receiving a digital image of a coating sample to be color flash matched, the digital image captured by a digital image capture system and represented by captured color image data formatted according to a first color model native to the digital image capture system;

[0164] converting the captured color image data into pixel-specific scintillation point data and pixel-specific background data, wherein the pixel-specific scintillation point data and the pixel-specific background data are formatted according to an advanced color appearance model corresponding to human color perception;

[0165] Calculating a first color flicker vision scale value of the coating sample to be color flicker matched from the pixel-specific flicker point data and the pixel-specific background data, wherein the first color flicker vision scale value indicates a color flicker characteristic of the coating sample to be color flicker matched;

[0166] retrieving a second colored flicker vision scale value for the candidate coating sample, wherein the second colored flicker vision scale value indicates a colored flicker characteristic of the candidate coating sample;

[0167] comparing the first colored flicker vision scale value to the second colored flicker vision scale value; and

[0168] A degree of match between the candidate coating sample and the coating sample to be color flash matched is determined based on the results of the comparing step.

[0169] Aspect 18. The computing system according to aspect 17, wherein the converting step further comprises:

[0170] converting the captured color image data into relative color image data formatted according to a relative tristimulus color model;

[0171] applying at least one scene photometric parameter to the relative color image data to obtain scene-adjusted color image data formatted according to an absolute tristimulus color model;

[0172] processing the scene-adjusted color image data to distinguish between constituent flicker point pixel data and constituent background pixel data, wherein the constituent flicker point pixel data and the constituent background pixel data are both formatted according to an absolute tristimulus color model;

[0173] converting the constituent scintillation point pixel data into pixel-specific scintillation point data formatted according to the advanced color appearance model; and

[0174] The constituent background pixel data is converted into pixel-specific background data formatted according to a high-level color appearance model.

[0175] Aspect 19. The computing system of aspect 17, wherein:

[0176] The calculating step further comprises determining a sparkle intensity value of the coating sample to be color-sparkle matched from the pixel-specific sparkle point data and the pixel-specific background data, wherein the sparkle intensity value indicates a high color contrast;

[0177] The calculating step further comprises determining a glitter texture value of the coating sample to be color glitter matched from the pixel-specific glitter point data and the pixel-specific background data; and

[0178] The first colored flicker visual scale value is calculated from the flicker intensity value and the flicker texture value.

[0179] Aspect 20. The computing system of aspect 17, wherein the method performed by the at least one processor further comprises:

[0180] calculating a visual scale difference between the first colored flicker visual scale value and the second colored flicker visual scale value;

[0181] comparing the visual scale difference to a visual scale threshold; and

[0182] When the visual scale difference is greater than the visual scale threshold, it is determined that the candidate coating sample and the coating sample to be colored flash matched are poorly matched.

[0183] Aspect 21. The computing system of aspect 20, wherein the method performed by the at least one processor further comprises:

[0184] When the visual scale difference is not greater than the visual scale threshold, calculating the color flicker difference between the candidate coating sample and the coating sample to be matched with the color flicker;

[0185] comparing the colored flicker difference to a colored flicker threshold; and

[0186] When the color flicker difference is greater than the color flicker threshold, it is determined that the candidate coating sample and the coating sample to be color flicker matched are poorly matched.

[0187] Aspect 22. The computing system of aspect 21, wherein the method performed by the at least one processor further comprises:

[0188] calculating a minimum color difference index based on pixel-based color model properties of the high color contrast flicker points identified for the candidate coating sample and for the coating sample to be colored flicker matched when the colored flicker difference is not greater than the colored flicker threshold;

[0189] Compare the minimum color difference index with the color difference threshold;

[0190] When the minimum color difference index is greater than the color difference threshold, determining that the candidate coating sample and the coating sample to be colored flash matched are poorly matched; and

[0191] When the minimum color difference index is not greater than the color difference threshold, it is determined that the candidate coating sample matches the coating sample to be matched in terms of color flicker.

[0192] Aspect 23. The computing system of aspect 22, wherein the method performed by the at least one processor further comprises:

[0193] In response to determining that the candidate coating sample matches the coating sample to be color flash matched, a production recipe associated with the candidate coating sample is retrieved.

[0194] Aspect 24. At least one non-transitory machine-readable storage medium storing instructions executable by at least one processor, the instructions being configurable to cause the at least one processor to perform a method of evaluating and matching the colored shimmering appearance of a coating containing at least one effect pigment type, the method comprising:

[0195] receiving a digital image of a coating sample to be color flash matched, the digital image captured by a digital image capture system and represented by captured color image data formatted according to a first color model native to the digital image capture system;

[0196] converting the captured color image data into pixel-specific scintillation point data and pixel-specific background data, wherein the pixel-specific scintillation point data and the pixel-specific background data are formatted according to an advanced color appearance model corresponding to human color perception;

[0197] Calculating a first color flicker vision scale value of the coating sample to be color flicker matched from the pixel-specific flicker point data and the pixel-specific background data, wherein the first color flicker vision scale value indicates a color flicker characteristic of the coating sample to be color flicker matched;

[0198] retrieving a second colored flicker vision scale value for the candidate coating sample, wherein the second colored flicker vision scale value indicates a colored flicker characteristic of the candidate coating sample;

[0199] comparing the first colored flicker vision scale value to the second colored flicker vision scale value; and

[0200] A degree of match between the candidate coating sample and the coating sample to be color flash matched is determined based on the results of the comparing step.

[0201] Although at least one exemplary embodiment has been presented in the foregoing detailed description, it should be appreciated that there are a large number of variations. It should also be appreciated that the exemplary embodiments or examples described herein are not intended to limit the scope, applicability, or configuration of the claimed subject matter in any way. On the contrary, the foregoing detailed description will provide a convenient roadmap for those skilled in the art to implement the embodiments or examples. It should be understood that various changes may be made to the function and arrangement of elements without departing from the scope defined by the claims, which includes known equivalents and foreseeable equivalents at the time of filing this patent application.

Claims

1. A computer-implemented method for evaluating and matching the colored shimmering appearance of a coating containing at least one effect pigment type, the method comprising: receiving a digital image of a coating sample to be color flash matched, the digital image captured by a digital image capture system and represented by captured color image data formatted according to a first color model native to the digital image capture system; converting the captured color image data into pixel-specific scintillation point data and pixel-specific background data, wherein the pixel-specific scintillation point data and the pixel-specific background data are formatted according to an advanced color appearance model corresponding to human color perception; Calculating a first color flicker vision scale value of the coating sample to be color flicker matched from the pixel-specific flicker point data and the pixel-specific background data, wherein the first color flicker vision scale value indicates a color flicker characteristic of the coating sample to be color flicker matched; retrieving a second colored flicker vision scale value for the candidate coating sample, wherein the second colored flicker vision scale value indicates a colored flicker characteristic of the candidate coating sample; comparing the first colored flicker vision scale value to the second colored flicker vision scale value; and A degree of match between the candidate coating sample and the coating sample to be color flash matched is determined based on the results of the comparing step.

2. The method according to claim 1, wherein the converting step further comprises: The captured color image data is converted to relative color image data formatted according to a relative tristimulus color model.

3. The method according to claim 2, wherein the converting step further comprises: At least one scene photometric parameter is applied to the relative color image data to obtain scene-adjusted color image data formatted according to an absolute tristimulus color model.

4. The method of claim 3, wherein the at least one scene photometric parameter comprises: (i) a light intensity value or illumination level associated with the viewing environment of the coating sample to be matched; (ii) the type of light associated with the viewing environment of the coating sample to be matched; or (iii) both (i) and (ii).

5. The method according to claim 3, wherein the converting step further comprises: The scene-adjusted color image data is processed to distinguish between constituent scintillation point pixel data and constituent background pixel data, wherein the constituent scintillation point pixel data and the constituent background pixel data are both formatted according to an absolute tristimulus color model.

6. The method according to claim 5, wherein the converting step further comprises: converting the constituent scintillation point pixel data into pixel-specific scintillation point data, the pixel-specific scintillation point data being formatted according to an advanced color appearance model; and The constituent background pixel data is converted into pixel-specific background data formatted according to a high-level color appearance model.

7. The method according to claim 1, wherein: The computing step further includes performing a segmentation routine on the pixel-specific scintillation point data and the pixel-specific background data to identify scintillation pixels that exhibit visually distinguishable scintillation effects that satisfy a color difference threshold based on a high-level color appearance model; and A first colored flicker vision scale value is calculated from the colored flicker characteristics of the identified flicker pixels.

8. The method according to claim 1, wherein: The calculating step further comprises determining a sparkle intensity value of the coating sample to be color-sparkle matched from the pixel-specific sparkle point data and the pixel-specific background data, wherein the sparkle intensity value indicates a high color contrast; The calculating step further comprises determining a glitter texture value of the coating sample to be color-sparkle matched from the pixel-specific glitter point data and the pixel-specific background data, wherein the glitter texture value indicates a physical density of glitter points; and The first colored flicker visual scale value is calculated from the flicker intensity value and the flicker texture value.

9. The method according to claim 8, wherein: The calculating step further comprises calculating a visual texture value from the flickering texture value; and The first colored flicker visual scale value is calculated from the flicker intensity value and the visual texture value.

10. The method according to claim 1, further comprising: calculating a visual scale difference between the first colored flicker visual scale value and the second colored flicker visual scale value; The visual scale difference is compared to a visual scale threshold; and When the visual scale difference is greater than the visual scale threshold, it is determined that the candidate coating sample and the coating sample to be colored flash matched are poorly matched.

11. The method according to claim 10, further comprising: When the visual scale difference is not greater than the visual scale threshold, calculating the color flicker difference between the candidate coating sample and the coating sample to be matched with the color flicker; comparing the colored flicker difference to a colored flicker threshold; and When the color flicker difference is greater than the color flicker threshold, it is determined that the candidate coating sample and the coating sample to be color flicker matched are poorly matched.

12. The method according to claim 11, further comprising: determining a sparkle intensity value for a coating sample to be color sparkle matched from the pixel-specific sparkle point data and the pixel-specific background data, wherein the sparkle intensity value indicates a high color contrast; determining a visual texture value of a coating sample to be color-sparkle matched from the pixel-specific scintillation point data and the pixel-specific background data, wherein the visual texture value indicates a visual density of the scintillation points; calculating a sparkle intensity difference between the sparkle intensity value of the candidate coating sample and the sparkle intensity value of the coating sample to be color sparkle matched; and calculating a visual texture difference between the visual texture value of the candidate coating sample and the visual texture value of the coating sample to be colored flash matched; The color flicker difference or spatial pattern difference is calculated as a function of flicker intensity difference and visual texture difference.

13. The method according to claim 11, further comprising: calculating a minimum color difference index based on pixel-based color model properties of the high color contrast flicker points identified for the candidate coating sample and for the coating sample to be colored flicker matched when the colored flicker difference is not greater than the colored flicker threshold; Compare the minimum color difference index with the color difference threshold; When the minimum color difference index is greater than the color difference threshold, it is determined that the candidate coating sample and the coating sample to be matched with colored flashes are poorly matched; and When the minimum color difference index is not greater than the color difference threshold, it is determined that the candidate coating sample matches the coating sample to be matched in terms of color flicker.

14. The method according to claim 13, wherein: (i) the method further includes, in response to determining that the candidate coating sample matches the coating sample to be color-flash matched, retrieving a production recipe associated with the candidate coating sample; (ii) the minimum color difference index is calculated based on a comparison of a colored flicker palette associated with a high color contrast flicker point; or (iii) Both (i) and (ii).

15. A computing system operable to evaluate and match the color and appearance of a coating containing at least one effect pigment type, the computing system comprising: at least one processor; and At least one non-transitory machine-readable storage medium storing instructions executable by the at least one processor, the instructions being configurable to cause the at least one processor to perform the following method, the method comprising the steps of: receiving a digital image of a coating sample to be color flash matched, the digital image captured by a digital image capture system and represented by captured color image data formatted according to a first color model native to the digital image capture system; converting the captured color image data into pixel-specific scintillation point data and pixel-specific background data, wherein the pixel-specific scintillation point data and the pixel-specific background data are formatted according to an advanced color appearance model corresponding to human color perception; Calculating a first color flicker vision scale value of the coating sample to be color flicker matched from the pixel-specific flicker point data and the pixel-specific background data, wherein the first color flicker vision scale value indicates a color flicker characteristic of the coating sample to be color flicker matched; retrieving a second colored flicker vision scale value for the candidate coating sample, wherein the second colored flicker vision scale value indicates a colored flicker characteristic of the candidate coating sample; comparing the first colored flicker vision scale value to the second colored flicker vision scale value; and A degree of match between the candidate coating sample and the coating sample to be color flash matched is determined based on the results of the comparing step.

16. The computing system of claim 15, wherein the converting step further comprises: converting the captured color image data into relative color image data formatted according to a relative tristimulus color model; applying at least one scene photometric parameter to the relative color image data to obtain scene-adjusted color image data formatted according to an absolute tristimulus color model; processing the scene-adjusted color image data to distinguish between constituent flicker point pixel data and constituent background pixel data, wherein the constituent flicker point pixel data and the constituent background pixel data are both formatted according to an absolute tristimulus color model; converting the constituent scintillation point pixel data into pixel-specific scintillation point data, the pixel-specific scintillation point data being formatted according to an advanced color appearance model; and The constituent background pixel data is converted into pixel-specific background data formatted according to a high-level color appearance model.

17. The computing system of claim 16, wherein: The calculating step further comprises determining a sparkle intensity value of the coating sample to be color-sparkle matched from the pixel-specific sparkle point data and the pixel-specific background data, wherein the sparkle intensity value indicates a high color contrast; The calculating step further comprises determining a glitter texture value of the coating sample to be colored glitter matched from the pixel-specific glitter point data and the pixel-specific background data; and The first colored flicker visual scale value is calculated from the flicker intensity value and the flicker texture value.

18. The computing system of claim 15, wherein the method performed by the at least one processor further comprises: calculating a visual scale difference between the first colored flicker visual scale value and the second colored flicker visual scale value; The visual scale difference is compared to a visual scale threshold; and When the visual scale difference is greater than the visual scale threshold, it is determined that the candidate coating sample and the coating sample to be colored flash matched are poorly matched.

19. The computing system of claim 18, wherein the method performed by the at least one processor further comprises: When the visual scale difference is not greater than the visual scale threshold, calculating the color flicker difference between the candidate coating sample and the coating sample to be matched with the color flicker; comparing the colored flicker difference to a colored flicker threshold; and When the color flicker difference is greater than the color flicker threshold, it is determined that the candidate coating sample and the coating sample to be color flicker matched are poorly matched.

20. The computing system of claim 19, wherein the method performed by the at least one processor further comprises: calculating a minimum color difference index based on pixel-based color model properties of the high color contrast flicker points identified for the candidate coating sample and for the coating sample to be colored flicker matched when the colored flicker difference is not greater than the colored flicker threshold; Compare the minimum color difference index with the color difference threshold; When the minimum color difference index is greater than the color difference threshold, it is determined that the candidate coating sample and the coating sample to be matched with colored flashes are poorly matched; and When the minimum color difference index is not greater than the color difference threshold, it is determined that the candidate coating sample matches the coating sample to be matched in terms of color flicker.

21. The computing system of claim 20, wherein the method performed by the at least one processor further comprises: In response to determining that the candidate coating sample matches the coating sample to be color flash matched, a production recipe associated with the candidate coating sample is retrieved.

22. At least one non-transitory machine-readable storage medium storing instructions executable by at least one processor, the instructions being configurable to cause the at least one processor to perform a method for evaluating and matching the colored shimmer appearance of a coating containing at least one effect pigment type, the method comprising: receiving a digital image of a coating sample to be color flash matched, the digital image captured by a digital image capture system and represented by captured color image data formatted according to a first color model native to the digital image capture system; converting the captured color image data into pixel-specific scintillation point data and pixel-specific background data, wherein the pixel-specific scintillation point data and the pixel-specific background data are formatted according to an advanced color appearance model corresponding to human color perception; Calculating a first color flicker vision scale value of the coating sample to be color flicker matched from the pixel-specific flicker point data and the pixel-specific background data, wherein the first color flicker vision scale value indicates a color flicker characteristic of the coating sample to be color flicker matched; retrieving a second colored flicker vision scale value for the candidate coating sample, wherein the second colored flicker vision scale value indicates a colored flicker characteristic of the candidate coating sample; comparing the first colored flicker vision scale value to the second colored flicker vision scale value; and A degree of match between the candidate coating sample and the coating sample to be color flash matched is determined based on the results of the comparing step.

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