Systems and methods for matching the color and appearance of a target coating

Abstract

The present disclosure provides processor-implemented systems and methods for matching the color and appearance of a target coating. The system includes a storage device for storing instructions and one or more data processors. The data processors are configured to execute the instructions to receive a target image of the target coating. The data processors are further configured to perform feature extraction analysis processing that divides the target image into a plurality of target pixels for image analysis.

Description

[0001] This application is a partial continuation of U.S. Patent Application No. 15 / 833,597, filed Dec. 6, 2017, which is incorporated herein by reference.

[0002] The technical field relates to coating technology, and more particularly, to systems and methods for matching the color and appearance of a target coating. BACKGROUND OF THE INVENTION

[0003] Visualization and selection of coatings with desired color and appearance play an important role in many applications. For example, paint suppliers must offer thousands of paints to cover the paint ranges of global OEM manufacturers for all current and most recent vehicle models. Providing such a large number of different paints as factory-packaged products increases the complexity of paint manufacturing and increases inventory costs. Accordingly, paint suppliers provide mixing systems that typically include 50 to 100 components (e.g., single pigment colors, binders, solvents, additives) and paint formulations for the components that match the range of coatings for vehicles. The mixing systems can reside at repair shops (i.e., body shops) or paint distributors and allow users to obtain a coating with the desired color and appearance by dispensing the components in amounts corresponding to the paint formulations. The paint formulations are typically stored in a database and distributed to customers via computer software by downloading or direct connection to an Internet database. Each paint formulation typically involves one or more alternative paint formulations to account for coating variations due to changes in vehicle production.

[0004] Identifying the paint formulation that most closely matches a target coating has become complicated due to such variations. For example, a particular coating may appear on three vehicle models that are produced at two assembly plants with different application equipment using paints from two OEM paint suppliers and over a lifespan of more than five model years. These sources of variation result in significant coating variations within the population of vehicles with that particular coating. The alternative paint formulations provided by the paint supplier match subsets of the color population such that a close match can be obtained for any vehicle that needs to be repaired. Each alternative paint formulation can be represented by a color swatch in a strip of color swatches, which enables a user to select the best-matching formulation by visual comparison to the vehicle.

[0005] Identifying the paint formulation that most closely resembles the target coating for repair is typically done by using a spectrophotometer or a fandeck. The spectrophotometer measures one or more color and appearance attributes of the target coating to be repaired. The color and appearance data is then compared with the corresponding data from the possible candidate formulations contained in a database. A candidate formulation whose color and appearance attributes best match the color and appearance attributes of the target coating to be repaired is then selected as the paint formulation most similar to the target coating. However, spectrophotometers are expensive and not readily available in the economic market.

[0006] Alternatively, a fandeck includes multiple sample coatings on pages or sheets within the fandeck. The sample coatings of the fandeck are then visually compared with the target coating being repaired. A formulation associated with the sample coating that best matches the color and appearance attributes of the target coating to be repaired is then selected as the paint formulation most similar to the target coating. However, fandecks 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.

[0007] Accordingly, it is desirable to provide systems and methods for matching the color and appearance of a target coating. Additionally, other desirable features and characteristics will become apparent from the following detailed description, the drawings, and the appended claims in conjunction with this background. Summary of the Invention

[0008] Various non - limiting embodiments of a system for matching the color and appearance of a target coating and various non - limiting embodiments of a method for matching the color and appearance of a target coating are disclosed herein. In one non - limiting embodiment, the system includes, but is not limited to, a storage device for storing instructions and one or more data processors. The one or more data processors are configured to execute instructions to receive a target image of the target coating. The data processors are also configured to apply a feature extraction analysis process to determine target image features and to use the target image features to determine a calculated matching sample image. The feature extraction analysis process includes analyzing a plurality of target pixels within the target image.

[0009] In the system as described above, the one or more data processors are configured to execute instructions to compare the sample image features of a sample image with the target image features.

[0010] In the system described above, one or more data processors are configured to apply a feature extraction analysis process, wherein the feature extraction analysis process determines one or more of the following: the L*a*b* color coordinates of each target pixel of a target image; the average L*a*b* color coordinates of the entire image of the target image based on each target pixel; the flash area of the black-and-white image of the target image; the flash intensity of the black-and-white image of the target image; the flash level of the black-and-white image of the target image; the flash color determination of the target image; the flash clustering of the target image; the flash color difference in the target image; the flash persistence of the target image, wherein the flash persistence is a measure of the flash as a function of one or more lighting changes during the capture of the target image; the color constancy at the target pixel level in the case of one or more lighting changes during the capture of the target image; the wavelet coefficients of the target image at the target pixel level; the Fourier coefficients of the target image at the target pixel level; the average color of a local area within the target image, wherein the local area can be one or more target pixels, but wherein the local area is less than the total area of the target image; the pixel count within a discrete L*a*b* range of the target image, wherein the L*a*b* range can be fixed or can be data-driven such that the range varies; the maximum filling coordinates of cubic bins at the target pixel level of the target image, wherein the cubic bins are based on a 3D coordinate mapping using L*a*b* or RGB values; the overall image color entropy of the target image; the image entropy of one or more L*a*b* planes in the L*a*b* plane as a function of the third dimension of the target image; the image entropy of one or more RGB planes in the RGB plane as a function of the third dimension of the target image; the local target pixel change metric of the target image; the roughness of the target image; the high variance vector of the target image, wherein principal component analysis is used to establish the high variance vector; and the high kurtosis vector of the target image, wherein independent component analysis is used to establish the high kurtosis vector.

[0011] In the system described above, one or more data processors are configured to execute instructions to retrieve a mathematical model to determine a computed matching sample image.

[0012] In the system described above, the mathematical model is a machine learning model.

[0013] In the system described above, one or more data processors are configured to determine a paint formulation corresponding to the computed matching sample image.

[0014] In the system described above, one or more data processors are configured to determine a plurality of paint formulations corresponding to the computed matching sample image, wherein the plurality of paint formulations includes paints of different grades.

[0015] In the system described above, one or more data processors are configured to reference a sample database to determine a paint formulation corresponding to a calculated matching sample image.

[0016] In the system described above, one or more data processors are configured to reference a sample database to determine a calculated matching sample image.

[0017] In the system described above, one or more data processors are configured to receive target image data of a target coating, wherein the target image data is associated with multiple images of the target coating having varying light angles relative to the imaging device.

[0018] In the system described above, one or more data processors are configured to receive target image data of a target coating, wherein the target image data is associated with multiple images of the target coating having varying magnifications.

[0019] In the system described above, the target coating is a metallic coating, a pearlescent coating, or a combination thereof.

[0020] The system described above further includes an imaging device, wherein the imaging device is configured to generate target image data of the target coating.

[0021] In the system described above, one or more data processors are further configured to retrieve a sample image from the sample database; extract sample image features from the sample image using feature extraction analysis processing; and generate one or more pre-specified matching criteria based on the sample image features.

[0022] In the system described above, one or more data processors are configured to determine a calculated matching sample image having a color that is substantially the same as the color of the target image.

[0023] In another non-limiting embodiment, the method includes, but is not limited to, obtaining, by one or more data processors, a target image of a target coating, wherein the target coating is an effect pigment-based coating. The target image is divided into a plurality of target pixels to be analyzed, and a calculated matching sample image is determined.

[0024] In the method described above, applying feature extraction analysis processing includes applying the following feature extraction analysis processing, where the feature extraction analysis processing includes one or more of the following processes: determining the L*a*b* color coordinates of each target pixel of the target image; determining the average L*a*b* color coordinates of the entire image of the target image based on each target pixel; determining the flash area of the black and white image of the target image; determining the flash intensity of the black and white image of the target image; determining the flash level of the black and white image of the target image; determining the flash color of the target image; determining the flash clustering of the target image; determining the flash color difference in the target image; determining the flash persistence of the target image, where the flash persistence is a measure of the flash as a function of one or more lighting changes during the capture of the target image; determining the color constancy at the target pixel level in the case of one or more lighting changes during the capture of the target image; determining the wavelet coefficients of the target image at the target pixel level; determining the Fourier coefficients of the target image at the target pixel level; determining the average color of a local area within the target image, where the local area can be one or more target pixels, but where the local area is less than the total area of the target image; determining the pixel count within a discrete L*a*b* range of the target image, where the L*a*b* range can be fixed or can be data-driven such that the range varies; determining the maximum filling coordinates of a cubic bin at the target pixel level of the target image, where the cubic bin is based on a 3D coordinate mapping using L*a*b* or RGB values; determining the overall image color entropy of the target image; determining the image entropy of one or more L*a*b* planes in the L*a*b* plane as a function of the third dimension of the target image; determining the image entropy of one or more RGB planes in the RGB plane as a function of the third dimension of the target image; determining the local target pixel change metric of the target image; determining the roughness of the target image; determining the high variance vector of the target image, where principal component analysis is used to establish the high variance vector; and determining the high kurtosis vector of the target image, where independent component analysis is used to establish the high kurtosis vector.

[0025] In the method described above, determining the calculated matching sample images includes determining a plurality of calculated matching sample images, where the plurality of calculated matching sample images include different grades of paint.

[0026] In the method described above, determining the calculated matching sample images includes comparing the sample image features of the sample images with the target image features.

[0027] The above method further includes determining the paint formulation corresponding to the calculated matching sample images, where the paint formulation includes effect additives. Description of the Drawings

[0028] Other advantages of the disclosed subject matter will be readily appreciated and will become better understood with reference to the following detailed description when considered in conjunction with the accompanying drawings, in which:

[0029] Figure 1 is a perspective view showing a non - limiting embodiment of a system for matching the color and appearance of a target coating;

[0030] Figure 2 is showing Figure 1 a block diagram of a non - limiting embodiment of the system;

[0031] Figure 3A is showing Figure 1 an image of a non - limiting embodiment of the target coating;

[0032] Figure 3B is showing Figure 3A a graphical representation of the RGB values of a non - limiting embodiment of the target coating;

[0033] Figure 4A is showing Figure 1 an image of a non - limiting embodiment of a first sample image of the system;

[0034] Figure 4B is showing Figure 4A a graphical representation of the RGB values of a non - limiting embodiment of the first sample image;

[0035] Figure 5A is showing Figure 1 an image of a non - limiting embodiment of a second sample image of the system;

[0036] Figure 5B is showing Figure 5A a graphical representation of the RGB values of a non - limiting embodiment of the second sample image;

[0037] Figure 6 is showing Figure 1 a perspective view of a non - limiting embodiment of an electro - imaging device of the system;

[0038] Figure 7 is showing Figure 1 another perspective view of a non - limiting embodiment of an electro - imaging device of the system;

[0039] Figure 8 is showing Figure 1 a flowchart of a non - limiting embodiment of the system;

[0040] Figure 9 is showing Figure 8 a flowchart of a non - limiting embodiment of the method;

[0041] Figure 10 is a flowchart showing Figure 8 another non - limiting embodiment of the method.

[0042] Figure 11 is a schematic diagram showing the formation of a cross - sectional portion of a substrate and a coating;

[0043] Figure 12 and Figure 13 is a schematic diagram showing the imaging of a capture coating;

[0044] Figure 14 and Figure 15 is a flowchart showing an embodiment of a system and method;

[0045] Figure 16 is a hypothetical diagram showing a possible part of a technique for determining target and / or sample characteristics;

[0046] Figures 17 to 19 is a flowchart showing an embodiment of a method; and

[0047] Figure 20 is a schematic diagram showing the application of a repair coating to a substrate. Detailed Description

[0048] The following detailed description includes examples and is not intended to limit the invention or its application and use. Additionally, no limitation is intended by any theory presented in the foregoing background or the following detailed description. It should be understood that in all the figures, corresponding reference numerals indicate the same or corresponding components and features.

[0049] Those skilled in the art will more readily appreciate the features and advantages recognized by the present disclosure by reading the following detailed description. It should be appreciated that: some features described in the context of different embodiments above and below for clarity may also be provided in combination in a single embodiment. Conversely, the various features described in the context of a single embodiment for brevity may also be provided separately or in any sub - combination. Additionally, unless the context clearly dictates otherwise, a reference to the singular may also include the plural (e.g., "a" and "an" may refer to one or one or more).

[0050] Unless otherwise clearly indicated, values within the specified ranges are used as approximations, with both the minimum and maximum values within the specified ranges being expressed with the word "about". In this way, minor variations above and below the stated ranges can be used to obtain substantially the same results as the values within the range. Moreover, the disclosure of these ranges is intended as a continuous range encompassing every value between the minimum and maximum values.

[0051] In this document, processes and techniques may be described in terms of functional and / or logical block components and with reference to symbolic representations of operations, processing tasks, and functions that may be performed by various computing components or devices. It should be appreciated that the various block components shown in the figures may be implemented by any number of hardware, software, and / or firmware components configured to perform the specified functions. For example, implementations of a system or component may use various integrated circuit components such as storage elements, digital signal processing elements, logic elements, look-up tables, etc. that can perform a variety of functions under the control of one or more microprocessors or other control devices.

[0052] The following description may refer to elements or nodes or features that are "coupled" together. As used herein, unless otherwise expressly stated, "coupled" means that an element / node / feature is directly or indirectly joined to another element / node / feature (or directly or indirectly communicates with another element / node / feature), and need not be joined mechanically. Thus, although the figures may depict an example of the arrangement of elements, there may be additional intervening elements, devices, features, or components in implementations of the described subject matter. Additionally, certain terms may be used in the following description for reference purposes only and are thus not intended to be limiting.

[0053] In this document, processes and techniques may be described in terms of functional and / or logical block components and with reference to symbolic representations of operations, processing tasks, and functions that may be performed by various computing components or devices. These operations, tasks, and functions are sometimes referred to as computer-executed, computerized, software-implemented, or computer-implemented. In fact, one or more processor devices may perform the described operations, tasks, and functions by manipulating electrical signals representing data bits at storage locations in system memory, as well as other signal processing. The storage locations where the data bits are held are physical locations having specific electrical, magnetic, optical, or organic characteristics corresponding to these data bits. It should be appreciated that the various block components shown in the figures may be implemented by any number of hardware, software, and / or firmware components configured to perform the specified functions. For example, implementations of a system or component may use various integrated circuit components such as storage elements, digital signal processing elements, logic elements, look-up tables, etc. that can perform a variety of functions under the control of one or more microprocessors or other control devices.

[0054] For the sake of brevity, conventional techniques regarding other functional aspects of graphics and image processing, touchscreen displays, and certain systems and subsystems (and their individual operating components) may not be described in detail herein. Additionally, the connecting lines shown in the various figures included herein are intended to represent examples of functional relationships and / or physical couplings between various elements. It should be noted that there may be many alternative or additional functional relationships or physical connections in implementations of the present subject matter.

[0055] As used herein, the term "module" refers to any hardware, software, firmware, electronic control component, processing logic, and / or processor device, either alone or in any combination, including but not limited to: application specific integrated circuits (ASICs), electronic circuits, processors (shared, dedicated, or grouped), memories that execute one or more software or firmware programs, combinational logic circuits, and / or other suitable components that provide the described functionality.

[0056] As used herein, the term "pigment" or "pigments" refers to one or more colorants that produce one or more colors. Pigments can be from natural or synthetic sources and are made of organic or inorganic components. Pigments can also include metal particles or flakes having specific or mixed shapes and sizes. Pigments are generally insoluble in coating compositions.

[0057] The term "effect pigment" or "effect pigments" refers to pigments that produce special effects in a coating. Examples of effect pigments include but are not limited to light-scattering pigments, light-interference pigments, and light-reflection pigments. Metal flakes such as aluminum flakes and pearlescent pigments such as mica pigments are examples of effect pigments.

[0058] The term "appearance" can include: (1) aspects of the visual experience of observing or identifying a coating; and (2) the perception that combines the spectral and geometric aspects of the coating with its illumination and viewing environment. Generally, appearance includes, in particular, the texture, graininess, sparkle, or other visual effects of the coating when observed from different viewing angles and / or under different illumination conditions. Appearance characteristics or appearance data can include but are not limited to: descriptions or measurement data regarding texture, metallic effect, pearlescent effect, gloss, image clarity, flake appearance and size (such as texture, graininess, sparkle, luminescence, and scintillation, and enhanced depth perception in the coating imparted by flakes, especially by metal flakes such as aluminum flakes). Appearance characteristics can be obtained by visual inspection or by using an appearance measurement device.

[0059] The term "color data" or "color characteristics" of a coating may include measured color data, which includes: spectral reflectance values, X, Y, Z values, L*, a*, b* values, L*, a*, b* values, L, C, h values, or combinations thereof. Color data may also include the color code, color name or description of a vehicle, or combinations thereof. Color data may even include the visual aspects of the color, chromaticity, hue, brightness or darkness of a coating. Color data can be obtained by visual inspection or by using a color measurement device such as a colorimeter, spectrophotometer or angular spectrophotometer. In particular, a spectrophotometer obtains color data by determining the wavelengths of light reflected by a coating. Color data may also include: descriptive data, such as the name of a color, the color code of a 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 measurement device; or export / import data files generated by a computing device or a color measurement device. Color data may also be generated by an appearance measurement device or a color appearance dual measurement device.

[0060] The term "coating" or "coating composition" may include any coating composition known to those skilled in the art and may include: two-component coating compositions, also known as "2K coating compositions"; one-component or 1K coating compositions; coating compositions having crosslinkable components and crosslinking components; radiation-curable coating compositions, such as UV-curable coating compositions or E-beam curable coating compositions; single-curing coating compositions; double-curing coating compositions; lacquer coating compositions; waterborne coating compositions or aqueous coating compositions; solvent-based coating compositions; or any other coating composition known to those skilled in the art. A coating composition can be formulated as a primer, basecoat or colored coating composition by incorporating desired pigments or effect pigments. A coating composition can also be formulated as a clear coating composition.

[0061] The term "vehicle", "automobile", "motor vehicle" or "motorized vehicle" may include motor vehicles such as sedans, buses, trucks, semi-trailer trucks, light trucks, SUVs (sport utility vehicles); tractors; motorcycles; trailers, ATVs (all-terrain vehicles); heavy machinery such as bulldozers, mobile cranes and excavators; airplanes; boats; ships; and other means of transportation.

[0062] The terms "formulation", "matched formulation", or "matching scheme" for a coating composition refer to a collection of information or instructions based on which a coating composition can be prepared. In one example, a matched formulation includes a list of the names and amounts of pigments, effect pigments, and other components of the coating composition. In another example, a matched formulation includes instructions on how to mix the various components of the coating composition.

[0063] As referred to herein Figure 1 A processor-implemented system 10 is provided for matching the color and appearance of a target coating 12. The target coating 12 may be on a substrate 14. The substrate 14 may be a vehicle or a part of a vehicle. The substrate 14 may also be any coated article including the target coating 12. The target coating 12 may include a colored coating, a transparent coating, or a combination of a colored coating and a transparent coating. The colored coating may be formed from a colored coating composition. The transparent coating may be formed from a transparent coating composition. The target coating 12 may be formed from one or more solvent-based coating compositions, one or more water-based coating compositions, one or more two-component coating compositions, or one or more one-component coating compositions. The target coating 12 may also be formed from one or more coating compositions each having a crosslinkable component and a crosslinking component, one or more radiation-curable coating compositions, or one or more lacquer coating compositions.

[0064] Refer to Figure 2 And continue to refer to Figure 1 , the system 10 includes an electronic imaging device 16 configured to generate target image data 18 of the target coating 12. The electronic imaging device 16 may be a device capable of capturing images over a wide range of electromagnetic wavelengths including visible or invisible wavelengths. The electronic imaging device 16 may also be defined as a mobile device. Examples of mobile devices include, but are not limited to: mobile phones (e.g., smart phones), mobile computers (e.g., tablet computers or laptop computers), wearable devices (e.g., smart watches or earphones), or any other type of device known in the art configured to receive the target image data 18. In one embodiment, the mobile device is a smart phone or a tablet computer.

[0065] In an embodiment, the electronic imaging device 16 includes a camera 20 (see Figure 7 ). The camera 20 may be configured to acquire the target image data 18. The camera 20 may be configured to capture images having visible wavelengths. The target image data 18 may be derived from a target image 58 of the target coating 12, such as a still image or a video. In certain embodiments, the target image data 18 is derived from a still image. In Figure 1In the illustrated embodiment, the electronic imaging device 16 is shown as being disposed near and spaced apart from the target coating 12. However, it should be understood that the electronic imaging device 16 of the embodiment is portable such that the electronic imaging device 16 can be moved to another coating (not shown). In other embodiments (not shown), the electronic imaging device 16 may be fixed in one position. In still other embodiments (not shown), the electronic imaging device 16 may be attached to a robotic arm for automated movement. In additional embodiments (not shown), the electronic imaging device 16 may be configured to simultaneously measure the characteristics of multiple surfaces.

[0066] The system 10 also includes a storage device 22 for storing instructions for performing the matching of the color and appearance of the target coating 12. The storage device 22 may store instructions executable by one or more data processors 24. The instructions stored in the storage device 22 may include one or more individual programs, each program including an ordered list of executable instructions for implementing a logical function. When the system 10 is operating, the one or more data processors 24 are configured to: execute the instructions stored within the storage device 22 to transfer data to and from the storage device 22, and generally control the operation of the system 10 in accordance with the instructions. In certain embodiments, the storage device 22 is associated with (or alternatively included within) any of: the electronic imaging device 16, a server associated with the system 10, a cloud computing environment associated with the system 10, or a combination thereof.

[0067] As described above, the system 10 also includes one or more data processors 24 configured to execute instructions. The one or more data processors 24 are configured to be communicatively coupled to the electronic imaging device 16. The one or more data processors 24 may be any customized or commercially available processor, central processing unit (CPU), auxiliary processor, semiconductor-based microprocessor (in the form of a microchip or chipset), or generally any device for executing instructions, among several processors associated with the electronic imaging device 16. The one or more data processors 24 may be communicatively coupled to any component of the system 10 by a wired connection, a wireless connection, and / or a device or a combination thereof. Examples of suitable wired connections include, but are not limited to: a hardware coupling, a splitter, a connector, a cable, or a wire. Examples of suitable wireless connections and devices include, but are not limited to: a Wi-Fi device, a Bluetooth device, a wide area network (WAN) wireless device, a Wi-Max device, a local area network (LAN) device, a 3G broadband device, an infrared communication device, an optical data transmission device, a radio transmitter and an optional receiver, a wireless telephone, a wireless telephone adapter card, or any other device that can transmit signals over a wide range of electromagnetic wavelengths including radio frequencies, microwave frequencies, visible wavelengths, or invisible wavelengths.

[0068] Refer to Figure 3A and Figure 3B , one or more data processors 24 are configured to execute instructions to receive, by the one or more data processors 24, target image data 18 of the target coating 12. As described above, the target image data 18 is generated by the electro-imaging device 16. The target image data 18 may define RGB values, L*a*b* values, or a combination thereof representing the target coating 12. In certain embodiments, the target image data 18 defines RGB values representing the target coating 12. The one or more data processors 24 may also be configured to execute instructions to convert the RGB values of the target image data 18 into L*a*b* values representing the target coating 12.

[0069] The target image data 18 includes target image features 26. The target image features 26 may include the color and appearance characteristics of the target coating 12, a representation of the target image data 18, or a combination thereof. In certain embodiments, the target image features 26 may include a representation based on image entropy.

[0070] One or more data processors 24 are configured to execute instructions to retrieve, by the one or more data processors 24, one or more feature extraction analysis processes 28' that extract the target image features 26 from the target image data 18. In an embodiment, the one or more feature extraction analysis processes 28' are configured to: identify a representation based on image entropy to extract the target image features 26 from the target image data 18. To this end, the one or more data processors 24 may be configured to execute instructions to identify a representation based on image entropy to extract the target image features 26 from the target image data 18.

[0071] Identifying a representation based on image entropy may include: determining a color image entropy curve of the target image data 18. The color entropy curve may represent the target image data 18 in three-dimensional L*a*b* space based on the Shannon entropy of each a*b* plane, each L*a* plane, each L*b* plane, or a combination thereof. The determination of the color entropy curve may include: dividing the three-dimensional L*a*b* space of the target image data 18 into a plurality of cubic subspaces; tabulating the cubic spaces with similar characteristics to obtain the total cubic space count for each characteristic; generating an empty image entropy array for each dimension of the three-dimensional L*a*b* space; and filling the empty image entropy array with the total cubic space count corresponding to each dimension.

[0072] The recognition of the image entropy-based representation may further include: determining the chromatic aberration image entropy curve of the target image data 18. The three-dimensional L*a*b* space can be used to represent the target image data 18 in the three-dimensional L*a*b* space with respect to the analysis of an alternative three-dimensional L*a*b* space. The determination of the chromatic aberration entropy curve may include: calculating the dL* image entropy, dC* image entropy, and dh* image entropy between the three-dimensional L*a*b* space and the alternative three-dimensional L*a*b* space.

[0073] The recognition of the image entropy-based representation may further include: determining the black-and-white intensity image entropy of the L* plane from the three-dimensional L*a*b* space of the target image data 18. The recognition of the image entropy-based representation may further include: determining the average L*a*b* value of the target image data 18. The recognition of the image entropy-based representation may further include: determining the L*a*b* value of the center of the densest cubic subspace.

[0074] One or more data processors 24 are further configured to execute the instructions described above to apply the target image data 18 to one or more feature extraction analysis processes 28'. One or more data processors 24 are further configured to execute the instructions described above to extract target image features 26 from the target image data 18 by using one or more feature extraction analysis processes 28'.

[0075] In an embodiment, the system 10 is configured to: extract the target image features 26 from the target image data 18 by recognizing the image entropy-based representation of the target image features 26. The recognition of the image entropy-based representation may include: determining the color image entropy curve of the target image data 18; determining the color image entropy curve of the target image data 18; determining the black-and-white intensity image entropy of the L* plane from the three-dimensional L*a*b* space of the target image data 18; determining the average L*a*b* value of the target image data 18; determining the L*a*b* value of the center of the densest cubic subspace; or a combination thereof.

[0076] Refer to Figure 4A and Figure 5A And continue to refer to Figure 2 , in an embodiment, the system 10 further includes a sample database 30. The sample database 30 may be associated with or separated from the electronic imaging device 16, for example, in a server-based environment or a cloud computing environment. It should be understood that one or more data processors 24 are configured to be communicatively coupled to the sample database 30. The sample database 30 may include a plurality of sample images 32, such as Figure 4A the first sample image 34 shown in Figure 5AThe second sample image 36 shown. In an embodiment, each of the plurality of sample images 32 is an image of a panel including a sample coating. Various sample coatings defining a set of paint formulations can be imaged to generate the plurality of sample images 32. One or more different electronic imaging devices 16 can be utilized to image the sample images 32 to account for variations in the imaging capabilities and performance of each electronic imaging device 16. The plurality of sample images 32 can be in any format, such as RAW, JPEG, TIFF, BMP, GIF, PNG, etc.

[0077] One or more data processors 24 can be configured to execute instructions to receive, by the one or more data processors 24, sample image data 38 of the sample images 32. The sample image data 38 can be generated by the electronic imaging device 16. The sample image data 38 can define RGB values, L*a*b* values, or a combination thereof representing the sample images 32. In certain embodiments, the sample image data 38 defines RGB values representing the sample images 32, such as Figure 4B the RGB values of the first sample image 34 shown and Figure 5B the RGB values of the second sample image 36 shown. One or more data processors 24 can also be configured to execute instructions to convert the RGB values of the sample image data 38 to L*a*b* values representing the sample images 32. The system 10 can be configured to normalize the sample image data 38 of the plurality of sample images 32 for various electronic imaging devices 16, thereby improving the performance of the system 10.

[0078] The sample image data 38 can include sample image features 40. The sample image features 40 can include color and appearance characteristics of the sample images 32, representations of the sample image data 38, or a combination thereof. In certain embodiments, the sample image features 40 can include a representation based on image entropy.

[0079] One or more data processors 24 are configured to execute instructions to retrieve, by the one or more data processors 24, one or more feature extraction analysis processes 28” that extract sample image features 40 from the sample image data 38. In an embodiment, the one or more feature extraction analysis processes 28” are configured to: identify a representation based on image entropy to extract sample image features 40 from the sample image data 38. To this end, one or more data processors 24 can be configured to execute instructions to identify a representation based on image entropy to extract sample image features 40 from the sample image data 38. It should be understood that the one or more feature extraction analysis processes 28” for extracting sample image features 40 can be the same as or different from the one or more feature extraction analysis processes 28’ for extracting target image features 26.

[0080] In an embodiment, system 10 is configured to extract sample image features 40 from sample image data 38 by identifying an image entropy-based representation of the sample image features 40. Identifying the image entropy-based representation may include: determining a color image entropy curve of the sample image data 38; determining a color image entropy curve of the sample image data 38; determining a black-and-white intensity image entropy of the L* plane of the three-dimensional L*a*b* space from the sample image data 38; determining an average L*a*b* value of the sample image data 38; determining an L*a*b* value of the center of the densest cubic subspace; or a combination thereof.

[0081] One or more data processors 24 are configured to execute instructions to retrieve a machine learning model 42 by the one or more data processors, and the machine learning model 42 identifies a computed matching sample image 44 from a plurality of sample images 32 using target image features 26. The machine learning model 42 may employ supervised training, unsupervised training, or a combination thereof. In an embodiment, the machine learning model 42 employs supervised training. Examples of suitable machine learning models include, but are not limited to: linear regression, decision tree, k-means clustering, principal component analysis (PCA), random decision forest, neural network, or any other type of machine learning algorithm known in the art. In an embodiment, the machine learning model is based on a random decision forest algorithm.

[0082] The machine learning model 42 includes a pre-specified matching criterion 46 representing the plurality of sample images 32 for identifying a computed matching sample image 44 from the plurality of sample images 32. In an embodiment, the pre-specified matching criterion 46 is arranged in one or more decision trees. One or more data processors 24 are configured to: apply the target image features 26 to the machine learning model 42. In an embodiment, the pre-specified matching criterion 46 is included in one or more decision trees, where the decision tree includes a root node, intermediate nodes through different levels, and end nodes. The target image features 26 can be processed through nodes to one or more end nodes, where each end node represents one of the plurality of sample images 32.

[0083] One or more data processors 24 are also configured to identify a computed matching sample image 44 based on substantially meeting one or more pre-specified matching criteria 46. In an embodiment, the phrase "substantially meet" means identifying the computed matching sample image 44 from a plurality of sample images 32 with the highest probability of matching the target coating 12. In an embodiment, the machine learning model 42 is based on a random decision forest algorithm including a plurality of decision trees, where the result of processing the target image features 26 for each decision tree is used to determine the probability that each sample image 32 matches the target coating 12. The sample image 32 with the highest probability of matching the target coating 12 can be defined as the computed matching sample image 44.

[0084] In an embodiment, one or more data processors 24 are configured to execute instructions to generate pre-specified matching criteria 46 for the machine learning model 42 based on sample image features 40. In certain embodiments, the pre-specified matching criteria 46 are generated based on sample image features 40 extracted from a plurality of sample images 32. One or more data processors 24 can be configured to execute instructions to train the machine learning model 42 based on a plurality of sample images 32 by generating pre-specified matching criteria 46 based on sample image features 40. The machine learning model 42 can be trained at regular time intervals (e.g., monthly) based on a plurality of sample images 32 included in the image database 30. As described above, the sample image features 40 can be utilized by identifying a representation based on image entropy to convert the sample image data 38 defining the RGB values of the sample image 32 into L*a*b* values.

[0085] The computed matching sample image 44 is used to match the color and appearance of the target coating 12. The computed matching sample image 44 can correspond to a paint formulation that may match the color and appearance of the target coating 12. The system 10 can include one or more alternative matching sample images 48 associated with the computed matching sample image 44. One or more alternative matching sample images 48 can be associated with the computed matching sample image 44 based on paint formulation, observed similarity, computed similarity, or a combination thereof. In certain embodiments, one or more alternative matching sample images 48 are associated with the computed matching sample image 44 based on paint formulation. In an embodiment, the computed matching sample image 44 corresponds to a primary paint formulation, and one or more alternative matching sample images 48 correspond to alternative paint formulations related to the primary paint formulation. The system 10 can include a visual matching sample image 50 that can be selected by a user from the computed matching sample image 44 and one or more alternative matching sample images 48 based on the similarity observed by the user to the target coating 12.

[0086] Reference Figure 6 and Figure 7 In an embodiment, the electro-imaging device 16 further includes a display 52 configured to display the calculated matching sample image 44. In certain embodiments, the display 52 is further configured to display an image 58 of the target coating 12 adjacent to the calculated matching sample image 44. In an embodiment, the display 52 is further configured to display one or more alternative matching sample images 48 associated with the calculated matching sample image 44. In an embodiment of the electro-imaging device 16 including the camera 20, the display 52 may be located opposite the camera 20.

[0087] In an embodiment, the system 10 further includes a user input module 54 configured to allow a user to select a visual matching sample image 50 from the calculated matching sample image 44 and one or more alternative matching sample images 48 based on the similarity of the target coating 12 observed by the user. In an embodiment of the electro-imaging device 16 including the display 52, the user may select the visual matching sample image 50 by a touch input on the display 52.

[0088] In an embodiment, the system 10 further includes a light source 56 configured to illuminate the target coating 12. In an embodiment of the electro-imaging device 16 including the camera 20, the electro-imaging device 16 may include the light source 56, and the light source 56 may be positioned adjacent to the camera 20.

[0089] In an embodiment, the system 10 further includes a light-tight enclosure (not shown) for isolating the target coating 12 to be imaged from external light, shadows, and reflections. The light-tight enclosure may be configured to house the electro-imaging device 16 and allow the target coating 12 to be exposed to the camera 20 and the light source 56. The light-tight enclosure may include a light diffuser (not shown) configured to cooperate with the light source 56 to sufficiently diffuse the light generated from the light source 56.

[0090] Reference is also made herein Figure 8 and continued reference is made Figures 1 to 7A method 1100 for matching the color and appearance of a target coating 12 is provided. The method 1100 includes the following steps 1102: receiving, by one or more data processors, target image data 18 of the target coating 12. The target image data 18 is generated by an electronic imaging device 16 and includes target image features 26. The method 1100 further includes the following steps 1104: retrieving, by one or more processors, one or more feature extraction analysis processes 28' for extracting the target image features 26 from the target image data 18. The method 1100 further includes the following steps 1106: applying the target image features 26 to the one or more feature extraction analysis processes 28'. The method 1100 further includes the following steps 1108: extracting the target image features 26 from the target image data 18 by using the one or more feature extraction analysis processes 28'.

[0091] The method 1100 further includes the following steps 1110: retrieving, by one or more data processors, a machine learning model 42 that uses the target image features 26 to identify a calculated matching sample image 44 from a plurality of sample images 32. The machine learning model 42 includes a pre-specified matching criterion 46 representing the plurality of sample images 32 for identifying the calculated matching sample image 44 from the plurality of sample images 32. The method 1100 further includes the following steps 1112: applying the target image features 26 to the machine learning model 42. The method 1100 further includes the following steps 1114: identifying the calculated matching sample image 44 based on substantially meeting one or more pre-specified matching criteria 46.

[0092] In an embodiment, the method 1100 further includes the following steps 1116: displaying, on a display 52, the calculated matching sample image 44, one or more alternative matching sample images 48 associated with the calculated matching sample image 44, and a target image 58 of the target coating 12 adjacent to the calculated matching sample image 44 and the one or more alternative matching sample images 48. In an embodiment, the method 1100 further includes the following steps 1118: selecting, by a user, a visual matching sample image 50 from the calculated matching sample image 44 and the one or more alternative matching sample images 48 based on the observed similarity to the target image data 18.

[0093] Referring to Figure 9 And continuing to refer to Figures 1 to 8, in an embodiment, method 1100 further includes the following step 1120: generating a machine learning model 42 based on a plurality of sample images 32. The step 1120 of generating the machine learning model 42 may include the following step 1122: retrieving a plurality of sample images 32 from the image database 30. The step 1120 of generating the machine learning model 42 may further include the following step 1124: extracting sample image features 40 from the plurality of sample images 32 based on one or more feature extraction analysis processes 28'. The step 1120 of generating the machine learning model 42 may further include the following step 1126: generating a pre-specified matching criterion 46 based on the sample image features 40.

[0094] Referring to Figure 10 and continuing to refer to Figures 1 to 9 , in an embodiment, method 1100 further includes the following step 1128: forming a coating composition corresponding to the calculated matching sample image 44. Method 1100 may further include the following step 1130: applying the coating composition to the substrate 14.

[0095] The method 1100 and system 10 disclosed herein can be used for any coated article or substrate 14 including a target coating 12. Some examples of such coated articles may include, but are not limited to: household appliances such as refrigerators, washing machines, dishwashers, microwave ovens, cooking and baking ovens; electronic devices such as televisions, computers, electronic gaming consoles, audio and video equipment; recreational equipment such as bicycles, skiing equipment, all-terrain vehicles; home or office furniture such as tables, filing cabinets; watercraft or ships such as boats, yachts or personal watercraft (PWC); airplanes; buildings; structures such as bridges; industrial equipment such as cranes, heavy trucks or bulldozers; or decorative articles.

[0096] Color matching of effect pigment-based coatings is particularly challenging. Effect coatings include metallic coatings and pearlescent coatings, but may also include other effects such as phosphorescence, fluorescence, etc. As Figure 11 shown and continuing to refer to Figures 1 to 10 , metallic coatings and pearlescent coatings include effect additives 74. Coatings including the target coating 12 and / or the sample coating 60 as shown may include several layers covering the substrate 14. The above-mentioned target coating 12 may include Figure 11layers identical to the exemplary coating 60 shown, and thus the description of the layers of the exemplary coating 60 also applies to the above-described target coating 12. As used herein, the term "overlying" means "over" such that an intermediate layer can be between an overlying component (in this example, the exemplary coating 60) and an underlying component (in this example, the substrate 14), or means "on" such that the overlying component physically contacts the underlying component. Additionally, the term "overlying" means that a vertical line passing through the overlying component also passes through the underlying component such that at least a portion of the overlying component is directly above at least a portion of the underlying component. It should be understood that the substrate 14 can be moved such that the relative "up" and "down" positions are changed. Spatial relative terms such as "top", "bottom", "above", and "below" are created in the context of the orientation of the Figure 11 cross-section. It should be understood that the spatial relative terms refer to the Figure 11 orientation in, and thus if the substrate 14 is oriented in another way, the spatial relative terms will still refer to the Figure 11 orientation depicted in. Thus, even if the substrate 14 is distorted, flipped, or oriented in some other way different from that depicted in the figures, the terms "above" and "below" remain the same.

[0097] Figure 11 Shown is a primer 62 overlying the substrate 14 and a basecoat 64 overlying the primer 62. In this specification, the primer 62 and the substrate 14 are not considered part of the exemplary coating 60. An optional effect coating 66 overlies the basecoat 64, and a clearcoat 68 overlies the optional effect coating 66. The exemplary coating formulation 70 includes a plurality of components 72, where the components 72 for the basecoat 64 can be different from the components 72 for the optional effect coating 66 and / or the clearcoat 68. One or both of the basecoat 64 and the effect coating 66 include an effect additive 74 as one of the components 72. The effect additive 74 is utilized to create a special effect for the exemplary coating 60, such as creating a metallic effect or a pearlescent effect. The exemplary coating formulation 70 includes a basecoat formulation 65 for the basecoat 64, an optional effect coating formulation 67 for the optional effect coating 66, and a clearcoat formulation 69 for the clearcoat 68. In some embodiments, the exemplary coating formulation 70 can also include formulations for other optional layers.

[0098] In the case where the sample coating 60 (or any other coating) includes a visible reflective sheet, a metallic effect is produced. The reflective sheet serves as the effect additive 74. In an embodiment, the metal particles in the paint absorb and reflect incident light of more colors than the color of the base paint, giving a coating with a varying appearance over a given area. Some coatings will appear as the color of the base coat, while other portions will reflect light and appear as a flash or sparkle. A metallic color is a color that appears as a polished metal color. The visual typically associated with metal is a metallic sheen that is different from a simple solid color. Metallic colors include a luminescent effect due to the brightness of the material, and this brightness varies with the change in the surface angle relative to the light source. One technique for producing a metallic effect color is to add aluminum flakes (which are an example of the effect additive 74) to the pigment coating. The aluminum flakes produce flashes that vary in size, brightness, and sometimes color depending on the treatment of the flakes. Larger flakes produce a coarser flash, while smaller flakes produce a finer flash. Different types of flakes can be used, such as flat and relatively round "silver dollar" flakes, like silver dollar coins. Other types of flakes can have serrated edges, like corn flakes. In some embodiments, the flakes can also be colored, so the flakes produce a colored flash.

[0099] Adding aluminum flakes to the base coat 64 produces a metallic effect, but if the same type and amount of flakes are added to the translucent effect coating 66 covering the base coat 64, the coating has a "deeper" different appearance. In another embodiment, the base coat 64 can include one type and amount of the effect additive 74, while the effect coating 66 can include a different type and / or amount of the effect additive 74 to produce an additional appearance. Thus, many variables affect the appearance of the metallic color, such as the type of flakes, the size of the flakes, the coating including the flakes, the base color, etc. Therefore, it is difficult to match the metallic effect due to the various possible factors and appearances.

[0100] Pearlescent coatings include effect additives 74 that selectively reflect, absorb, and / or transmit visible light, which can produce a colored appearance that varies based on the structure and morphology of the flakes. This gives the coating a flash and a dark color that vary with the viewing angle and / or the lighting angle. The effect additives 74 in the pearlescent coatings can be ceramic crystals, and these effect additives 74 can be added to the base coat 64, the effect coating 66, or both. Additionally, the pearlescent effect additives 74 can be of varying grades with different sizes, refractive indices, shapes, etc., and different grades can be used alone or in combination. The pearlescent effect additives 74 can also be combined with the metallic effect additives 74 in various combinations, and the appearance of the coating will change with the change in the concentration, type, location, etc. of the effect additives. All the different possible variations in the effect additives 74 can be applied to a single color, so the technique of only matching colors will be ineffective in reproducing the appearance of effect pigment-based coatings.

[0101] As described above, since the coating based on the effect pigment has a varying appearance, as seen in the illustrations of Figure 3A , Figure 4A and Figure 5A , different pixels within the image will have different colors, brightness, hues, chromas, or other appearance characteristics. Thus, a color matching protocol that decomposes the image into pixels and then analyzes the image pixel by pixel to determine the pixel differences between two or more pixels can help match the overall appearance of the coating based on the effect pigment. The characteristics of the image can be determined based on the differences between the pixels within the image, using a mathematical model such as the machine learning model described above. In this way, as described above, the target image 58 can be analyzed pixel by pixel using a mathematical model to generate the target image characteristics 26, and this can be combined with other target image characteristics 26 such as color (which can be determined by treating the target image 58 as a whole rather than pixel by pixel) or other target image characteristics. Again, as described above, the resulting one or more target image characteristics 26 can be compared with a sample database 30 that has generated similar sample image characteristics 40 to find the best match. Pixel-by-pixel evaluation can result in a match for the effect pigment, which is not possible in the case of an evaluation based on treating the target image 58 as a whole.

[0102] Referring to Figure 12 the illustrated embodiment and continuing to refer to Figures 1 to 11 . As described above, the imaging device 16 captures the target image 58 of the target coating 12. During the capture of the target image 58, the imaging device 16 is set at an imaging angle 76 with respect to the surface of the target coating 12, and the illumination source 78 is set at an illumination angle 80. The target image 58 is divided into a plurality of target pixels 82, where the plurality of target pixels 82 vary such that at least one target pixel 82 is different in appearance from another target pixel 82.

[0103] In a similar manner, referring to Figure 13 the illustrated embodiment and continuing to refer to Figures 1 to 12 , again as described above, the imaging device 16 captures the sample image 32 of the sample coating 60. The imaging device 16 used to capture the sample image 32 can be the same as the imaging device 16 used to capture the target image 58, but different imaging devices 16 can also be utilized. The illumination source 78 is also used to capture the sample image 32, where, similar to Figure 12As described for the target image 58, the imaging device is set at an imaging angle 76 relative to the surface of the sample coating 60, while the illumination source 78 is set at an illumination angle 80. The sample image 32 is divided into a plurality of sample pixels 84, and one sample pixel 84 has a different appearance from another sample pixel 84. In an embodiment, the sample coating 60 includes an effect additive 74, which is shown as small dots in the illustration of the sample image 32, and the effect additive 74 produces a change in the appearance of the sample pixels 84 within the sample image 32. Figure 12 and Figure 13 The target coating 12 and the sample coating 60 in respectively may include a primer coat 64, an optional effect coat 66, and a clear coat 68, but variations in the coatings are also possible.

[0104] As Figure 14 shown in the embodiment of and continuing to refer to Figures 1 to 13 , a sample database 30 is generated for matching the target image 58 with the sample paint formulation 70. Generating the sample database 30 includes generating the sample paint formulation 70, wherein the sample paint formulation 70 includes an effect additive 74. The sample paint formulation 70 may include a primer coat 64 and a clear coat 68, or a primer coat 64, an effect coat 66, and a clear coat 68, but other embodiments are also possible. For example, any one of the primer coat 64, the optional effect coat 66, and the clear coat 68 may include multiple layers, and there may also be other coatings.

[0105] Once the sample paint formulation 70 is generated, the sample coating 60 is produced using the sample paint formulation 70. This is typically done by applying the materials from the sample paint formulation 70 to the substrate 14, for example, via spraying, applying with a brush, dip coating, digital printing, or any other coating technique. In an embodiment, the sample coating 60 is formed by spraying, where the spraying utilizes the recommended spraying conditions for the paint grade in the sample paint formulation 70. The spraying conditions may include the solvent type, the amount of solvent, the spray gun pressure, the type and / or size of the nozzle of the spray gun used, the distance between the spray gun and the substrate 14, etc. Using the same techniques commonly used in an automotive body shop or other situations where the target coating 12 may need to be matched to produce the sample coating 60 can provide a more accurate representation of the finished product that can be expected compared to the case where the sample coating 60 is applied using another technique. Since the sample paint formulation 70 includes one or more effect additives 74, the sample coating 60 has a varying appearance, where a portion of the sample coating 60 appears different from another portion. For example, a gloss finish appears different from a matte color.

[0106] Then, using the imaging device 16, a sample image 32 is generated, for example, by photographing the sample coating 60. The sample image 32 includes a plurality of sample image data 38, such as RGB values, L*a*b* values, etc. The sample image 32 can be one or more still images or moving images. In an embodiment, the sample image 32 includes a plurality of still images captured at known and specified illumination and imaging angles, the distance between the imaging device 16 and the sample coating 60, and the illumination angle. The sample image 32 can also be for a substantially flat portion of the sample coating 60, but in some embodiments, the sample image can also include one or more images of the sample coating 60 having a known curvature.

[0107] A feature extraction analysis process 28 can be applied to the sample image data 38 to generate sample image features 40. Then the sample image features 40 and the sample paint formulation 70 are associated and stored in the sample database 30. The sample database 30 can include a plurality of sample image features 40 associated with one sample paint formulation 70, and one or more sample images 32 can also be associated with the sample paint formulation 70. The sample database 30 is stored in one or more storage devices 22, and the one or more storage devices 22 can be the same as or different from the above-mentioned storage devices 22 for matching the target coating 12. As described above, the storage device 22 storing the sample database 30 can be associated with the data processor 24 to execute instructions for storing data and retrieving data from the sample database 30. The process shown in Figure 14 can be repeated for a plurality of sample paint formulations 70, so that a plurality of sample paint formulations 70 are stored in the sample database 30. The sample paint formulation 70 can also be configured to closely match the original equipment coating provided by the vehicle manufacturer, so that the vehicle coating matches the sample paint formulation 70. One or more sample images 32 can be used as the calculated matching sample images 44, or different parameter sets can be used to capture the calculated matching sample images 44 from the sample coating 60.

[0108] A plurality of sample paint formulations 70 can be generated, and the plurality of sample paint formulations 70 produce substantially the same sample image 32, where the sample paint formulations 70 are different from each other and include different grades of paint. Some vehicle repair shops tend to use one grade of paint, and the grade of paint can vary from one vehicle repair shop to the next. Since vehicle repair shops have the skills and familiarity with using a specific grade of paint, a sample database 30 including matching sample paint formulations 70 with the same grade of paint as the grade of paint familiar to the vehicle repair shop can improve the results. In this way, different vehicle repair shops (or other users of the sample database 30) that typically use different grades of paint can utilize the same sample database 30.

[0109] In Figure 15 the embodiment of and continuing to refer to Figures 1 to 14 more particularly illustrates the feature extraction analysis process 28" described above for Figure 14 The sample image 32 is divided into a plurality of sample pixels 84. Each sample pixel 84 has sample pixel image data, and the sample pixel image data is used to determine the sample pixel feature 86 of the sample pixel 84. Since the sample pixel 84 may have an appearance different from that of the overall sample image 32, the sample pixel feature 86 may be different from the overall sample feature 40. The sample pixel feature 86 may be a determination of the RGB value of the sample pixel 84 or the L*a*b* value of the sample pixel 84 or other appearance attributes of the sample pixel 84. In an embodiment, the sample pixel features 86 are determined for all the sample pixels 84 of the sample image 32, but in an alternative embodiment, the sample pixel features 86 may be determined only for a subset of the sample pixels 84 of the sample image 32. In all embodiments, the sample pixel features 86 are determined for a plurality of sample pixels 84, wherein the sample pixel features 86 vary for at least some of the sample pixels 84. Then, the sample pixel feature differences 88 are determined for the sample pixels 84. Then, the sample image feature 40 is determined based on the sample pixel feature differences 88.

[0110] In Figure 16 and continuing to refer to Figures 1 to 15 illustrates an embodiment using a three-dimensional coordinate system 90. The three-dimensional coordinate system 90 may represent an RGB color system, wherein one axis of the three-dimensional coordinate system 90 is the R value, another axis is the G value, and the third axis is the B value. Alternatively, the three-dimensional coordinate system 90 may represent an L*a*b* color system, wherein one axis is the L* value, another axis is the a* value, and the third axis is the b* value. In an alternative embodiment, the three-dimensional coordinate system 90 may represent other axes, and in some embodiments, the three-dimensional coordinate system 90 may not be used. Then, the sample pixel features 86 may be plotted in the three-dimensional coordinate system 90, and the number of sample pixels 84 falling within each block of the three-dimensional coordinate system 90 may be recorded. In Figure 16 the imaginary illustration of, the first block closest to the 0-0-0 coordinate has a count of 1, the block directly above the first block has a count of 2, and the block directly above the block with a count of 2 has a count of 5. Alternative techniques may be used to determine the sample pixel feature differences 88, and multiple techniques may be used to determine the multiple sample pixel feature differences 88 of a sample image 32 in multiple ways.

[0111] Then, as Figure 15 shown and continuing to refer to Figures 1 to 14 and Figure 16, the sample image feature 40 can be determined based on the sample pixel feature difference 88. Since the sample image feature 40 is based on the color or appearance changes in different spaces within the sample image 32, determining the sample image feature 40 based on multiple sample pixel feature differences 88 is referred to as spatial micro - color analysis in this document. There can be multiple sample image features 40 determined for a single sample image 32, and some of those sample image features 40 can be based on spatial micro - color analysis while others may not be. However, in this specification, at least one of the sample image features 40 is based on spatial micro - color analysis. In this way, the sample database 30 includes at least one sample image feature 40 based on spatial micro - color analysis. Figure 15 The sample image data 38 block at the top of Figure 15 and the sample image feature 40 block at the bottom of Figure 14 illustrate the implementation of the steps utilized between the identically - labeled blocks in

[0112] Reference Figure 17 to the illustrated implementation and continue to refer to Figures 1 to 16 . Figure 17 illustrates an implementation for utilizing spatial micro - color analysis on a target image 58, as exemplified by the feature extraction analysis process 28’ block in Figure 2 . Thus, Figure 17 the target image data 18 block at the top of Figure 16 and the target image feature 26 block third from the bottom of Figure 2 are obtained from the identically - named blocks in Figure 17 also illustrates an implementation where the mathematical model 100 for comparing the target image feature 26 with the sample image feature 40 is a model other than the machine - learning model 42. However, it should be understood that the machine - learning model 42 can still be utilized in some implementations. As described above, Figure 2 and Figure 17 the sample image features 40 shown in

[0113] As in Figure 12 and Figure 17As shown, the target image 58 is divided into a plurality of target pixels 82. Each target pixel 82 includes target pixel image data, where the target pixel image data is at least a part of the target image data 18. As described above for the sample pixel feature 86, the target pixel feature 102 is determined according to the target pixel 82, where each target pixel in the target pixel 82 has at least some of the target image data in the target image data 18, and at least some of the target image data in the target image data 18 varies for different target pixels 82. Again, as described above for the sample pixel feature 86, then, the target pixel feature difference 104 is determined according to the target pixel feature 102, and then, the target image feature 26 is determined according to the target pixel feature difference 104. Thus, because at least some of the target pixels 82 have different target image data 18, at least one of the target image features 26 is determined by spatial micro-color analysis. The target image feature 26 of a single target image 58 may include one or more target image features 26 determined by spatial micro-color analysis, and may also include one or more target image features 26 not based on spatial micro-color analysis. Then, as described above, the calculated matching sample image 44 is determined using the pre-specified matching criterion 46.

[0114] A variety of spatial micro-color analysis mathematical techniques can be utilized to determine target image features 26 and / or sample image features 40, and in some embodiments the same techniques can be utilized to facilitate matching. A partial list of spatial micro-color analysis mathematical techniques is provided below, where the general term pixel means a target pixel 82 or a sample pixel 84, and an image means a target image 58 or a sample image 32. Examples of spatial micro-color analysis mathematical techniques include, but are not limited to: determining the L*a*b* color coordinates of each pixel of an image; determining the average L*a*b* color coordinates of each pixel based on the entire image of an image; determining the flash regions of the black-and-white image of an image; determining the flash intensity of the black-and-white image of an image; determining the flash level of the black-and-white image of an image; determining the flash color of an image; determining the flash clustering of an image; determining the flash color difference in an image; determining the flash persistence of an image, where flash persistence is a measure of the flash as a function of one or more lighting changes during the capture of the image; determining color constancy at the pixel level in the case of one or more lighting changes during the capture of the image; determining the wavelet coefficients of an image at the pixel level; determining the Fourier coefficients of a target image at the target pixel level; determining the average color of a local region within an image, where the local region can be one or more pixels, but where the local region is less than the total area of the image; determining the pixel count within a discrete L*a*b* range of an image, where the L*a*b* range can be fixed or can be data-driven such that the range varies; determining the maximum fill coordinates of cubic bins at the pixel level of an image, where the cubic bins are based on a 3D coordinate mapping using L*a*b* or RGB values; determining the overall image color entropy of an image; determining the image entropy of one or more L*a*b* planes in the L*a*b* plane as a function of the third dimension of an image; determining the image entropy of one or more RGB planes in the RGB plane as a function of the third dimension of an image; determining a local pixel change metric of an image; determining the roughness of an image; determining the high variance vector of an image, where principal component analysis is used to establish the high variance vector; and determining the high kurtosis vector of an image, where independent component analysis is used to establish the high kurtosis vector.

[0115] Determining the L*a*b* color coordinates of each pixel of the target / sample image includes decomposing the image into pixels and then determining the L*a*b* color coordinates of a plurality (or in some embodiments, all) of the pixels. Determining the average L*a*b* color coordinate of each pixel of the full image of the image means taking the mean of the L*a*b* samples for each pixel within the image. Determining the flash region of the black-and-white image of the image means capturing or obtaining the black-and-white image and then determining the number of pixels including the flash and the total number of pixels within a given region. The given region can be the entire image or a subset of the image. Then, the flash region is determined by dividing the number of pixels including the flash in the given region by the total number of pixels in the given region. Determining the flash intensity of the black-and-white image of the image means determining the average luminance of the pixels including the flash within a given region.

[0116] Determining the flash level of the black-and-white image means determining a value that describes the visual perception of the flash phenomenon based on the flash region and the flash intensity. Determining the flash color of the image means determining the location of the flash and then determining the color of the flash. Determining the flash clustering of the image means using a clustering or distribution fitting algorithm to determine the various colors of the flashes present in the image. As described above, determining the flash color difference within the image means determining the color of the flash in the image and then determining the change or difference in that color in the flash. Determining the flash persistence of the image means determining whether the flash remains within a given pixel (and whether the flash has a change in luminance) when the illumination changes during the capture of the image and the imaging angle 76 and the illumination angle 80 remain the same. Determining the color constancy at the pixel level means determining whether the color of the pixels within a given pixel remains the same when the illumination changes during the capture of the image and the imaging angle 76 and the illumination angle 80 remain the same. At least two different images are required to determine the flash persistence and the color constancy.

[0117] Determining the Fourier coefficients of the image at the pixel level means determining the coefficients associated with the image after decomposing the image into sine components of different frequencies using a Fourier transform, where the coefficients describe the frequencies present in the image. This can be determined for a given number of pixels within a given region of the image. Determining the wavelet coefficients of the image at the pixel level means determining the coefficients associated with the image after decomposing the image into components associated with shifted and scaled versions of wavelets using a discrete or continuous wavelet transform, where the coefficients describe the image content associated with the shifted and scaled versions of the wavelets. A wavelet is a function that tends to zero at its extrema and contains oscillations in a local region. This can be determined for a given number of pixels within a given region of the image. Determining the average color of a local region within the image means determining the average color of one or more pixels within a sample region that is smaller than the total region of the image. Determining the pixel count within the discrete L*a*b* range of the image means determining the pixel count within a given region of a three-dimensional coordinate system 90 using the L*a*b* values as axes, asFigure 16 is similar to the illustration. The size of the given region within the three-dimensional coordinate system 90 can be fixed, or the size can vary according to the generated count value. Determine the maximum filling coordinates of the cubic bins at the pixel level of the image, where the cubic bins are based on the three-dimensional coordinate system 90 using L*a*b* or RGB values as axes, meaning to determine the block with the highest pixel count as shown Figure 16 Determine the value that designates a block as being "maximally" filled, which can be done using a set value, a percentage value, or other metrics.

[0118] Determining the overall image color entropy of an image means determining the overall color entropy of the image based on the variations within the pixels. The color entropy can be Shannon entropy or other types of entropy calculations. Determining the image entropy of one or more planes in the L*a*b* plane or the RGB plane as a function of the third dimension of the image means selecting a plane within the three-dimensional coordinate system 90 as shown Figure 16 and then determining the entropy based on the values along that plane. As described above, the entropy can be Shannon entropy or other types of entropy calculations. Determining the local pixel change metric of an image means selecting substantially any pixel feature and then determining the variation of that pixel feature within the image. Determining the roughness of an image means determining the impression of image roughness based on shadows or other features that imply roughness. Determining the high variance vector of an image means determining a vector based on the vector origin at the coordinate origin, where principal component analysis is used to establish the high variance vector. The high variance vector can be considered a target image feature 26 or a sample image feature 40, or the pixel data from the image can be projected onto the vector to obtain a new target image feature 26 or a sample image feature 40. Determining the high kurtosis vector of an image still means determining a vector based on the vector origin at the coordinate origin, where independent component analysis is used to establish the high kurtosis vector. The high kurtosis vector can be considered a target image feature 26 or a sample image feature 40, or the pixel data from the image can be projected onto the vector to obtain a new target image feature 26 or a sample image feature 40.

[0119] As Figure 18 shown and continuing to refer to Figures 1 to 17, in another embodiment, a method for matching a target coating 12 is provided. The method includes obtaining 1200 a target image 58 of the target coating 12, where the target coating 12 is an effect pigment-based coating including an effect additive 74. The method further includes applying 1210 a feature extraction analysis process 28' to the target image 58, where the feature extraction analysis process 28' includes: dividing 1220 the target image 58 into a plurality of target pixels 82 including target pixel image data; determining 1230 the target pixel features 102 of each target pixel of the plurality of target pixels 82; determining 1240 the target pixel feature differences 104 between the respective target pixels 82; determining 1250 a target image feature 26 based on the target pixel feature differences 104; and calculating 1260 a calculated matching sample image 44 using the target image feature 26 based on substantially meeting one or more pre-specified matching criteria.

[0120] As Figure 19 shown and continuing to refer to Figures 1 to 18 , a method for generating a sample database 30 is also provided. The method includes preparing 1300 a sample coating 60 according to a sample coating formulation 70 including an effect additive 74 such that the appearance of the sample coating 60 varies from one location to another. The method further includes imaging 1310 the sample coating to generate a sample image 32 including sample image data, where the sample image 32 is divided into a plurality of sample pixels 84, each sample pixel including sample pixel image data. The next step is to retrieve 1320 one or more sample image features 40 from the sample image data, where at least one of the sample image features 40 includes a spatial micro-color analysis, and the spatial micro-color analysis includes a value determined by a sample pixel feature difference 88 between at least two sample pixels among the sample pixels 84. Another step is to store 1330 the sample coating formulation 70 and one or more sample image features 40 in the sample database 30, where the sample coating formulation 70 is associated with the one or more sample image features 40.

[0121] Referring to Figure 20Once the calculated matching sample image 44 is obtained, it can be approved by the operator. At this time, the sample paint formulation 70 corresponding to the calculated matching sample image 44 can be used to prepare the repair paint 110. In an exemplary embodiment, the sample paint formulation 70 includes an effect additive 74 and one or more other components 72. The repair paint 110 can be applied to the substrate 14, such as a vehicle in need of repair, using one or more of a variety of techniques. In an exemplary embodiment, as shown, the repair paint 110 is applied to the substrate 12 using a digital printing press 112 and digital printing techniques, but in alternative embodiments, the repair paint 110 can be applied to the substrate 12 by other techniques including but not limited to spraying, applying with a brush, and / or dip coating.

[0122] Although at least one embodiment has been presented in the foregoing detailed description, it should be understood that there are numerous variations. It should also be understood that one or more embodiments are merely examples and are not intended to limit the scope, applicability, or configuration in any way. On the contrary, the foregoing detailed description will provide those skilled in the art with a convenient way to implement the embodiments, and it should be understood that various changes can be made to the functions and arrangements of the elements described in the embodiments without departing from the scope set forth in the appended claims and their legal equivalents.

Claims

1. A system for matching a target coating, comprising: a storage device for storing instructions; one or more data processors configured to execute instructions to: receive a target image of the target coating, wherein the target image includes target image data; apply feature extraction analysis processing to the target image data to determine target image features, wherein the feature extraction analysis processing includes: dividing the target image into a plurality of target pixels, determining target pixel features of each of the plurality of target pixels, determining target pixel feature differences between the respective target pixels, and using the target pixel feature differences to determine the target image features, wherein the target image features include a representation based on image entropy, and wherein the feature extraction analysis processing further includes determining a color image entropy curve of the target image data; and based on meeting one or more pre-specified matching criteria, using the target image features to determine a calculated matching sample image, wherein the calculated matching sample image has a color that is almost the same as the color of the target image.

2. The system according to claim 1, wherein, The one or more data processors are configured to execute instructions to compare the sample image features of a sample image with the target image features.

3. The system according to claim 1, wherein The one or more data processors are configured to apply the feature extraction analysis processing, wherein the feature extraction analysis processing determines one or more of the following: The L*a*b* color coordinates of each target pixel of the target image; the average L*a*b* color coordinates of the entire image of the target image according to each target pixel; the flash area of the black-and-white image of the target image; the flash intensity of the black-and-white image of the target image; the flash level of the black-and-white image of the target image; the flash color determination of the target image; the flash clustering of the target image; the flash color difference in the target image; the flash persistence of the target image, where the flash persistence is a measure of the flash as a function of one or more lighting changes during the capture of the target image; the color constancy at the target pixel level in the case of one or more lighting changes during the capture of the target image; the wavelet coefficients of the target image at the target pixel level; the Fourier coefficients of the target image at the target pixel level; the average color of a local area within the target image, where the local area is one or more target pixels, but where the local area is less than the total area of the target image; the pixel count within a discrete L*a*b* range of the target image, where the L*a*b* range is fixed or data-driven such that the range varies; the maximum fill coordinates of the cubic bins at the target pixel level of the target image, where the cubic bins are based on a 3D coordinate mapping using L*a*b* or RGB values; the overall image color entropy of the target image; the image entropy of one or more L*a*b* planes in the L*a*b* plane as a function of the third dimension of the target image; the image entropy of one or more RGB planes in the RGB plane as a function of the third dimension of the target image; the local target pixel change metric of the target image; the roughness of the target image; the high variance vector of the target image, where the high variance vector is established using principal component analysis; and the high kurtosis vector of the target image, where the high kurtosis vector is established using independent component analysis.

4. The system according to claim 1, wherein The one or more data processors are configured to determine a paint formulation corresponding to the computed matching sample image.

5. The system according to claim 1, wherein, The one or more data processors are configured to receive the target image data of the target coating, where the target image data is associated with a plurality of images of the target coating having varying light angles relative to the imaging device.

6. The system according to claim 1, wherein The one or more data processors are configured to receive the target image data of the target coating, where the target image data is associated with a plurality of images of the target coating having varying magnifications.

7. The system according to claim 1, wherein The one or more data processors are further configured to retrieve sample images from a sample database; extract sample image features from the sample images using the feature extraction analysis process; and generate the one or more pre-specified matching criteria based on the sample image features.

8. A method of matching a target coating, comprising: A target image of the target coating is obtained by one or more data processors, wherein the target coating is a coating based on effect pigments, and wherein the target image includes target image data; The target image data is subjected to feature extraction analysis processing by one or more data processors, wherein the feature extraction analysis processing includes: Dividing the target image into a plurality of target pixels; Determining the target pixel features of each of the plurality of target pixels; Determining the target pixel feature differences between the respective target pixels; and Determining target image features based on the target pixel feature differences, wherein the target image features include a representation based on image entropy, and wherein the feature extraction analysis processing further includes determining a color image entropy curve of the target image data; and Based on meeting one or more pre-specified matching criteria, using the target image features to determine a calculated matching sample image, wherein the calculated matching sample image has a color that is almost the same as the color of the target image.

9. The method according to claim 8, further comprising: Determining a sample coating formulation corresponding to the calculated matching sample image, wherein the sample coating formulation includes effect additives; Preparing a repair coating using the sample coating formulation corresponding to the calculated matching sample image; and Applying the repair coating to a substrate.

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