System and method for matching the color and appearance of a target coating

Abstract

The present invention provides a system and method for matching the color and appearance of a target coating. The system includes an electronic imaging device configured to receive target image data of the target coating. The target image data includes target coating features. The system also includes one or more feature extraction algorithms for extracting target image features from the target image data. The system also includes a machine learning model for identifying a calculated matching sample image from a plurality of sample images using the target image features. The machine learning model includes a predetermined matching criterion representing a plurality of sample images for identifying the calculated matching sample image from the plurality of sample images. The calculated matching sample image is used to match the color and appearance of the target coating.

Description

[0001] FIELD OF THE DISCLOSURE The present disclosure relates to systems and methods for matching the color and appearance of a target coating, and more particularly, to systems and methods for identifying a matching sample image having a predetermined similarity to visual data of a target coating. Background Art

[0002] Visualization and selection of coatings with desired colors and appearance play an important role in many applications. For example, coating suppliers must provide thousands of coatings to cover the range of coatings for global OEM manufacturers of all current and latest model vehicles. Providing a large number of different coatings as factory packaged products increases the complexity of coating manufacturing and increases inventory costs. Therefore, coating suppliers provide a mixer system that usually includes 50 to 100 components (for example, single pigment color, adhesive, solvent, additive), and the mixer system has a coating formula of components that match the range of coatings of the vehicle. The mixer can be resident in a repair shop (that is, a body repair shop) or a coating dealer, and allows users to obtain coatings with desired color and appearance by dispensing components in an amount corresponding to the coating formula. The coating formula is usually stored in a database and distributed to customers via computer software by downloading or directly connecting to an Internet database. Each formula in the coating formula usually involves one or more alternative coating formulas to solve the coating changes caused by changes in vehicle production.

[0003] Identifying the paint formula that is most similar to the target coating is complicated by this variation. For example, a particular paint may appear on three vehicle models produced in two assembly plants with different application equipment using paints from two OEM paint suppliers and over a lifespan of five model years. These sources of variation result in significant paint variation for the population of vehicles with that particular paint. Alternative paint formulas offered by paint suppliers are matched to subsets of the color population so that a close match is available for any vehicle that needs repair. Each of the alternative paint formulas can be represented by a color swatch in the Midland Strip Color Card, which enables the user to select the best matching formula by visual comparison to the vehicle.

[0004] Identification is usually accomplished by using a spectrophotometer or a fandeck to identify the coating formulation most similar to the target coating to be repaired. A spectrophotometer measures one or more colors and appearance attributes of the target coating to be repaired. The color and appearance data are then compared with the corresponding data from the possible candidate formulations contained in the database. The 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 coating formulation most similar to the target coating. However, spectrophotometers are expensive and not easily available in the economic market.

[0005] Alternatively, the Midland color strip chart includes multiple sample paint layers on a page or sheet within the Midland color strip chart. The sample paint layers of the Midland color strip chart are then visually compared to the target coating being repaired. The formula associated with the sample paint layer that best matches the color and appearance attributes of the target coating to be repaired is then selected as the paint formula that is most similar to the target coating. However, the Midland color strip chart is cumbersome to use and difficult to maintain because of the large number of sample paint layers required to address all of the paint on vehicles on the road today.

[0006] Therefore, 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 subsequent summary and detailed description and the appended claims, taken in conjunction with the accompanying drawings and this background. Summary of the invention

[0007] Disclosed herein are various non-limiting embodiments of systems for matching the color and appearance of a target coating and various non-limiting embodiments of methods for matching the color and appearance of a target coating.

[0008] In a non-limiting embodiment, the system includes but is not limited to a storage device for storing instructions for performing matching of the color and appearance of the target coating. The system also includes but is not limited to one or more data processors configured to execute instructions. One or more data processors are configured to execute instructions to receive target image data of the target coating by one or more data processors. The target image data is generated by an electronic imaging device and includes target image features. One or more data processors are configured to execute instructions to retrieve one or more feature extraction algorithms for extracting target image features from the target image data by one or more processors. One or more data processors are configured to execute instructions to apply the target image data to one or more feature extraction algorithms. One or more data processors are configured to execute instructions to extract target image features from the target image data using one or more feature extraction algorithms. One or more data processors are configured to execute instructions to retrieve a machine learning model by one or more data processors, which machine learning model uses the target image features to identify the calculated matching sample image from a plurality of sample images. The machine learning model includes a predetermined matching criterion representing a plurality of sample images for identifying the calculated matching sample image from a plurality of sample images. The one or more data processors are configured to execute instructions to apply the target image features to the machine learning model. The one or more data processors are configured to execute instructions to identify a calculated matching sample image based on substantial satisfaction of one or more of the predetermined matching criteria. The calculated matching sample image is used to match the color and appearance of the target coating.

[0009] In another non-limiting embodiment, the method includes, but is not limited to, receiving target image data of a target coating by one or more data processors. The target image data is generated by an electronic imaging device and includes target image features. The method also includes, but is not limited to, retrieving, by one or more processors, one or more feature extraction algorithms that extract target image features from the target image data. The method also includes, but is not limited to, applying the target image data to one or more feature extraction algorithms. The method also includes, but is not limited to, extracting target image features from the target image data using one or more feature extraction algorithms. The method also includes, but is not limited to, retrieving, by one or more data processors, a machine learning model that uses the target image features to identify a calculated matching sample image from a plurality of sample images. The machine learning model includes a predetermined matching criterion representing a plurality of sample images for identifying the calculated matching sample image from a plurality of sample images. The method also includes, but is not limited to, applying the target image features to the machine learning model. The method also includes, but is not limited to, identifying a calculated matching sample image based on substantial satisfaction of one or more of the predetermined matching criteria. The calculated matching sample image is used to match the color and appearance of the target coating. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Other advantages of the disclosed subject matter will be readily appreciated as they become better understood by referring to the following detailed description when considered in conjunction with the accompanying drawings, in which:

[0011] Figure 1 is a perspective view illustrating a non-limiting embodiment of a system for matching the color and appearance of a target coating;

[0012] Figure 2 It shows Figure 1 A block diagram of a non-limiting embodiment of a system;

[0013] Figure 3A It shows Figure 1 An image of a non-limiting embodiment of a target coating;

[0014] Figure 3B It shows Figure 3A A graphical representation of the RGB values ​​of a non-limiting embodiment of a target coating;

[0015] Figure 4A It shows Figure 1 an image of a non-limiting embodiment of a first sample image of a system;

[0016] Figure 4B It shows Figure 4A a graphical representation of RGB values ​​of a non-limiting embodiment of a first sample image;

[0017] Figure 5A It shows Figure 1 an image of a non-limiting embodiment of a second sample image of a system;

[0018] Figure 5B It shows Figure 5A a graphical representation of RGB values ​​of a non-limiting embodiment of a second sample image;

[0019] Figure 6 It shows Figure 1 A perspective view of a non-limiting embodiment of an electronic imaging device of a system;

[0020] Figure 7 It shows Figure 1 Another perspective view of a non-limiting embodiment of an electronic imaging device of a system;

[0021] Figure 8 It shows Figure 1 A flowchart of a non-limiting embodiment of a system;

[0022] Fig. 9 It shows Figure 8 A flowchart of a non-limiting embodiment of a method of; and

[0023] Fig.10 It shows Figure 8 A flow chart of another non-limiting embodiment of a method of. DETAILED DESCRIPTION

[0024] The following detailed description is merely exemplary in nature and is not intended to limit the invention or the application and use of the invention. In addition, it is not intended to be bound by any theory presented in the foregoing background or the following detailed description. It should be understood that throughout the drawings, corresponding reference numerals indicate similar or corresponding parts and features.

[0025] By reading the following detailed description, it will be easier for those of ordinary skill in the art to understand the features and advantages determined in the present disclosure. It should be understood that, for the sake of clarity, certain features described above and below in the context of a separate embodiment may also be provided in a single embodiment in a combined manner. On the contrary, for the sake of brevity, the various features described in the context of a single embodiment may also be provided individually or in any sub-combination. In addition, unless the context clearly indicates otherwise, reference to the singular may also include the plural (e.g., "a" and "an" may refer to one or one or more).

[0026] Although the minimum and maximum values ​​within the stated ranges are preceded by the word "about", unless otherwise expressly stated, the numerical values ​​used in the various ranges specified in this disclosure are stated as approximate values. In this manner, slight variations above and below the stated ranges can be used to obtain substantially the same results as the values ​​within the stated ranges. Moreover, the disclosure of these ranges is intended as a continuous range that includes every value between the minimum and maximum values.

[0027] Techniques and techniques may be described herein 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 understood 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 systems or components may employ various integrated circuit components, such as memory elements, digital signal processing elements, logic elements, lookup tables, etc., that may perform various functions under the control of one or more microprocessors or other control devices.

[0028] The following description may refer to elements or nodes or features that are "coupled" together. As used herein, unless otherwise expressly stated, "coupling" means: an element / node / feature is directly or indirectly joined to (or directly or indirectly communicates with) another element / node / feature, and not necessarily mechanically joined to another element / node / feature. Therefore, although the accompanying drawings may depict an exemplary arrangement of elements, additional insertion elements, devices, features or parts may be present in the embodiment of the depicted subject matter. In addition, certain terms may also be used in the following description only for the purpose of reference, and are therefore not intended to be limited.

[0029] Skills and techniques can be described herein according to functions and / or logic block components and with reference to symbolic representations of operations, processing tasks and functions that can be performed by various computing components or devices. Such operations, tasks and functions are sometimes referred to as computer-implemented, computerized, software-implemented or computer-implemented. In fact, one or more processor devices can perform the described operations, tasks and functions by manipulating electrical signals representing data bits at memory locations in system memory and other processing of signals. The memory location that stores data bits is a physical location with specific electrical, magnetic, optical or organic properties corresponding to the data bits. It should be understood that the various block components shown in the figure can be implemented by any number of hardware, software and / or firmware components configured to perform a specified function. For example, the implementation of a system or component can use various integrated circuit components that can perform various functions under the control of one or more microprocessors or other control devices, such as memory elements, digital signal processing elements, logic elements, lookup tables, etc.

[0030] For the sake of brevity, conventional techniques related to other functional aspects of graphics and image processing, touch screen displays, and certain systems and subsystems (and their respective operating components) may not be described in detail herein. In addition, the connecting lines shown in the various figures included herein are intended to represent exemplary functional relationships and / or physical couplings between various elements. It should be noted that many alternative or additional functional relationships or physical connections may appear in embodiments of the present subject matter.

[0031] As used herein, the term "module" refers to any hardware, software, firmware, electronic control component, processing logic and / or processor device, alone or in any combination, including but not limited to: application specific integrated circuits (ASICs), electronic circuits, (shared, dedicated or group) processors and memory that execute one or more software or firmware programs, combined logic circuits and / or other appropriate components that provide the described functionality.

[0032] As used herein, the term "pigment" or "pigments" refers to one or more colorants that produce one or more colors. Pigments can come from natural or synthetic sources and can be made of organic or inorganic ingredients. Pigments can also include metal particles or flakes with specific or mixed shapes and dimensions. Pigments are generally insoluble in the coating composition.

[0033] The term "effect pigment" or "effect pigments" refers to a pigment that produces a special effect in a coating. Examples of effect pigments include, but are not limited to, light scattering pigments, light interference pigments, and light reflecting pigments. Metallic flakes such as aluminum flakes and pearlescent pigments such as mica-based pigments are examples of effect pigments.

[0034] The term "appearance" may include: (1) aspects of the visual experience through which a coating is viewed or recognized; and (2) the perception of spectral and geometric aspects of a coating in conjunction with its lighting and viewing environment. Typically, appearance includes the texture, roughness, sparkle, or other visual effects of a coating, particularly when viewed from varying viewing angles and / or under varying lighting conditions. Appearance characteristics or appearance data may include, but are not limited to, descriptions or measurement data regarding texture, metallic effects, pearlescent effects, gloss, differences in images, flake appearance and size (such as texture, roughness, sparkle, shine, and glitter), and the enhancement of depth perception in a coating imparted by flakes, particularly produced by metallic flakes such as aluminum flakes. Appearance characteristics may be obtained by visual inspection or by using an appearance measurement device.

[0035] The term "color data" or "color characteristics" of a coating may include measured color data including: spectral reflectance values, X, Y, Z values, L, a, b values, L*, a*, b* values, L, C, h values ​​or a combination thereof. The color data may also include a color code of a vehicle, a color name or description or a combination thereof. The color data may even include visual aspects of the color, chromaticity, hue, brightness or darkness of the coating. The color data may be obtained by visual inspection or by using a color measuring device such as a colorimeter, a spectrophotometer or an angle spectrophotometer. In particular, a spectrophotometer obtains color data by determining the wavelength of light reflected by a coating layer. The color data may also include: descriptive data such as the name of a color, a color code of a vehicle; a binary, textured or encrypted data file containing descriptive data of one or more colors; a measurement data file such as a measurement data file generated by a color measuring device; or an export / import data file generated by a computing device or a color measuring device. The color data may also be generated by an appearance measuring device or a color appearance dual measuring device.

[0036] The term "coating" or "coating composition" may include any coating composition known to those skilled in the art, and may include: a two-component coating composition, also referred to as a "2K coating composition"; a one-component or 1K coating composition; a coating composition having a crosslinkable component and a crosslinking component; a radiation-curable coating composition, such as a UV-curable coating composition or an E-beam-curable coating composition; a single-cure coating composition; a dual-cure coating composition; a lacquer coating composition; a waterborne coating composition or an aqueous coating composition; a solvent-borne coating composition; or any other coating composition known to those skilled in the art. The coating composition may be formulated as a primer, basecoat, or colored coating composition by incorporating the desired pigment or effect pigment. The coating composition may also be formulated as a clearcoat composition.

[0037] The terms "vehicle," "automobile," "motor vehicle," or "motor vehicle" may include: automobiles, such as cars, buses, trucks, semi-trucks, pickup trucks, SUVs (sport utility vehicles); tractors; motorcycles; trailers; ATVs (all-terrain vehicles); heavy-duty moving vehicles, such as bulldozers, mobile cranes and earthmovers; aircraft; large ships; small boats, as well as other modes of transportation.

[0038] The term "formula", "matching formula" or "matching recipe" for a coating composition refers to a collection of information or instructions based on which a coating composition can be prepared. In one example, a matching formula includes a list of names and quantities of pigments, effect pigments and other components of the coating composition. In another example, a matching formula includes instructions on how to mix the various components of the coating composition.

[0039] In this article, refer to Figure 1 A system 10 for matching the color and appearance of a target coating 12 is provided. The target coating 12 can be on a substrate 14. The substrate 14 can be a part of a vehicle or a vehicle. The substrate 14 can also be any coating product including the target coating 12. The target coating 12 can include a color coating layer, a clear coating layer, or a combination of a color coating layer and a clear coating layer. The color coating layer can be formed by a color coating composition. The clear coating layer can be formed by a clear coating composition. The target coating 12 can be formed by 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 single-component coating compositions. The target coating 12 can also be formed by 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 paint coating compositions.

[0040] Reference 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 can be a device that can capture images at a wide range of electromagnetic wavelengths including visible wavelengths or invisible wavelengths. The electronic imaging device 16 can also be defined as a mobile device. Examples of mobile devices include, but are not limited to: a mobile phone (e.g., a smart phone), a mobile computer (e.g., a tablet computer or a laptop computer), a wearable device (e.g., a smart watch or a headset), or any other type of device known in the art that is configured to receive the target image data 18. In an exemplary embodiment, the mobile device is a smart phone or a tablet computer.

[0041] In an embodiment, the electronic imaging device 16 includes a camera 20 (see Figure 7 ). The camera 20 can be configured to acquire target image data 18. The camera 20 can be configured to capture images having visible wavelengths. The target image data 18 can be derived from an image 58 of the target coating 12, such as a still image or a video. In some embodiments, the target image data 18 is derived from a still image. Figure 1In the exemplary embodiment shown, the electronic imaging device 16 is shown as being arranged near the target coating 12 and spaced apart from the target coating 12. However, it should be understood that the electronic imaging device 16 of the exemplary embodiment is portable, so that the electronic imaging device 16 can be moved to another coating (not shown). In other embodiments (not shown), the electronic imaging device 16 can be fixed at one position. In other other embodiments (not shown), the electronic imaging device 16 can be attached to a robot arm to move automatically. In other embodiments (not shown), the electronic imaging device 16 can be configured to measure the characteristics of multiple surfaces simultaneously.

[0042] 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 can store instructions that can be executed by one or more data processors 24. The instructions stored in the storage device 22 can include one or more separate programs, each program including an ordered list of executable instructions for implementing logical functions. When the system 10 is working, the one or more data processors 24 are configured to: execute the instructions stored in the storage device 22 to transfer data to and from the storage device 22, and generally control the operation of the system 10 according to the instructions. In some embodiments, the storage device 22 is associated with (or alternatively included in) the following: an electronic imaging device 16, a server associated with the system 10, a cloud computing environment associated with the system 10, or a combination thereof.

[0043] As introduced 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 can be any custom or commercially available processor among several processors associated with the electronic imaging device 16, a central processing unit (CPU), an auxiliary processor, a semiconductor-based microprocessor (in the form of a microchip or chipset), or generally any device for executing instructions. The one or more data processors 24 can be communicatively coupled to any component of the system 10 via 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: hardware coupling, a splitter, a connector, a cable, or a wire. Examples of suitable wireless connections and devices include, but are not limited to, Wi-Fi devices, Bluetooth devices, wide area network (WAN) wireless devices, Wi-Max devices, local area network (LAN) devices, 3G broadband devices, infrared communication devices, optical data transmission devices, radio transmitters and optionally receivers, wireless telephones, wireless telephone adapter cards, or any other device that can transmit signals at a wide range of electromagnetic wavelengths including radio frequencies, microwave frequencies, visible wavelengths, or invisible wavelengths.

[0044] Reference Figure 3A and Figure 3B , the one or more data processors 24 are configured to execute instructions to receive target image data 18 of the target coating 12 by the one or more data processors 24. As described above, the target image data 18 is generated by the electronic 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.

[0045] The target image data 18 includes target image features 26. The target image features 26 may include color characteristics 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 an image entropy-based representation.

[0046] The 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 algorithms 28' for extracting target image features 26 from the target image data 18. In an embodiment, the one or more feature extraction algorithms 28' are configured to: identify an image entropy-based representation for extracting 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 an image entropy-based representation for extracting the target image features 26 from the target image data 18.

[0047] Identifying the image entropy-based representation may include determining a color image entropy curve for the target image data 18. The target image data 18 may be represented in a three-dimensional L*a*b* space using a color entropy curve based on a Shannon entropy for each of the a*b* planes, a Shannon entropy for each of the L*a* planes, a Shannon entropy for each of the L*b* planes, or a combination thereof. Determination of the color entropy curve may include partitioning the three-dimensional L*a*b* space of the target image data 18 into a plurality of cubic subspaces; tabulating cubic spaces with similar characteristics to derive a 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.

[0048] Identifying the image entropy-based representation may also include determining a color difference image entropy curve for the target image data 18. The target image data 18 may be represented in the three-dimensional L*a*b* space using the three-dimensional L*a*b* space analyzed with respect to the alternative three-dimensional L*a*b* space. Determination of the color difference entropy curve may include calculating 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.

[0049] Identifying the image entropy-based representation may also include determining a black and white intensity image entropy of an L* plane from the three-dimensional L*a*b* space of the target image data 18. Identifying the image entropy-based representation may also include determining an average L*a*b* value of the target image data 18. Identifying the image entropy-based representation may also include determining an L*a*b* value of the center of the densest cubic subspace.

[0050] The one or more data processors 24 are further configured to execute the instructions described above to apply the target image data 18 to the one or more feature extraction algorithms 28'. The 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 using the one or more feature extraction algorithms 28'.

[0051] In an exemplary embodiment, the system 10 is configured to extract the target image feature 26 from the target image data 18 by identifying an image entropy-based representation of the target image feature 26. Identifying the image entropy-based representation may include determining a color image entropy curve for the target image data 18, determining a color image entropy curve for the target image data 18, determining a black and white intensity image entropy of an L* plane from a three-dimensional L*a*b* space of the target image data 18, determining an average L*a*b* value for the target image data 18, determining an L*a*b* value of the center of a densest cubic subspace, or a combination thereof.

[0052] Reference Figure 4A and Figure 5A And continue to refer to Figure 2 In an embodiment, the system 10 further includes an image database 30. The image database 30 may be associated with the electronic imaging device 16 or separate from the electronic imaging device 16, for example, in a server-based environment or a cloud computing environment. It should be understood that the one or more data processors 24 are configured to be communicatively coupled to the image database 30. The image database 30 may include a plurality of sample images 32, for example Figure 4A The first sample image 34 and Figure 5AA second sample image 36 is 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 coating formulations may be imaged to generate the plurality of sample images 32. The sample images 32 may be imaged using one or more different electronic imaging devices 16 to account for variations in imaging capabilities and performance of each of the electronic imaging devices 16. The plurality of sample images 32 may be in any format, such as RAW, JPEG, TIFF, BMP, GIF, PNG, etc.

[0053] The one or more data processors 24 may be configured to execute instructions to receive sample image data 38 of the sample image 32 by the one or more data processors 24. The sample image data 38 may be generated by the electronic imaging device 16. The sample image data 38 may define RGB values, L*a*b* values, or a combination thereof representing the sample image 32. In some embodiments, the sample image data 38 defines, for example, a first sample image 34. Figure 4B The second sample image 36 shown Figure 5B RGB values ​​representing the sample image 32 are shown. The one or more data processors 24 may also be configured to execute instructions to convert the RGB values ​​of the sample image data 38 into L*a*b* values ​​representing the sample image 32. The system 10 may be configured to standardize the sample image data 38 of a plurality of sample images 32 for various electronic imaging devices 16, thereby improving the performance of the system 10.

[0054] The sample image data 38 may include sample image features 40. The sample image features 40 may include color characteristics and appearance characteristics of the sample image 32, a representation of the sample image data 38, or a combination thereof. In some embodiments, the sample image features 40 may include a representation based on image entropy.

[0055] One or more data processors 24 are configured to execute instructions to retrieve one or more feature extraction algorithms 28' for extracting sample image features 40 from the sample image data 38 by the one or more data processors 24. In an embodiment, the one or more feature extraction algorithms 28' are configured to: identify an image entropy-based representation for extracting sample image features 40 from the sample image data 38. To this end, the one or more data processors 24 may be configured to execute instructions to identify an image entropy-based representation for extracting sample image features 40 from the sample image data 38. It should be understood that the one or more feature extraction algorithms 28' for extracting sample image features 40 may be the same as or different from the one or more feature extraction algorithms 28' for extracting target image features 26.

[0056] In an exemplary embodiment, the system 10 is configured to extract the sample image features 40 from the 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 for the sample image data 38, determining a color image entropy curve for the sample image data 38, determining a black and white intensity image entropy of an L* plane from a three-dimensional L*a*b* space of the sample image data 38, determining an average L*a*b* value for the sample image data 38, determining an L*a*b* value of the center of a densest cubic subspace, or a combination thereof.

[0057] One or more data processors 24 are configured to execute instructions to retrieve, by one or more data processors, a machine learning model 42 that utilizes 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 may employ supervised training, unsupervised training, or a combination thereof. In an exemplary embodiment, the machine learning model 42 employs supervised training. Examples of suitable machine learning models include, but are not limited to, linear regression, decision trees, k-means clustering, principal component analysis (PCA), random decision forests, neural networks, or any other type of machine learning algorithm known in the art. In an exemplary embodiment, the machine learning model is based on a random decision forest algorithm.

[0058] The machine learning model 42 includes a predetermined matching criterion 46 representing multiple sample images 32 for identifying a calculated matching sample image 44 from the multiple sample images 32. In an embodiment, the predetermined matching criterion 46 is arranged in one or more decision trees. One or more data processors 24 are configured to apply the target image feature 26 to the machine learning model 42. In an exemplary embodiment, the predetermined matching criterion 46 is included in one or more decision trees, and the decision tree includes a root node, intermediate nodes throughout different levels, and terminal nodes. The target image feature 26 can be processed by a node to one or more of the terminal nodes, each of which represents one of the multiple sample images 32.

[0059] The one or more data processors 24 are further configured to identify the calculated matching sample image 44 based on substantially satisfying one or more of the predetermined matching criteria 46. In an embodiment, the state “substantially satisfied” means that the calculated matching sample image 44 is identified from the plurality of sample images 32 by having a maximum probability for matching the target coating 12. In an exemplary embodiment, the machine learning model 42 is based on a random decision forest algorithm including a plurality of decision trees, wherein the result of each decision tree is used to determine the probability of each sample image 32 matching the target coating 12 through processing of the target image features 26. The sample image 32 having the maximum probability of matching the target coating 12 can be defined as the calculated matching sample image 44.

[0060] In an embodiment, one or more data processors 24 are configured to execute instructions to generate a predetermined matching criterion 46 of the machine learning model 42 based on the sample image features 40. In certain embodiments, the predetermined matching criterion 46 is generated based on the sample image features 40 extracted from the plurality of sample images 32. One or more data processors 24 may be configured to execute instructions to train the machine learning model 42 based on the plurality of sample images 32 by generating the predetermined matching criterion 46 based on the sample image features 40. The machine learning model 42 may be trained at regular time intervals (e.g., monthly) based on the plurality of sample images 32 included in the image database 30. As described above, the sample image data 38 defining RGB values ​​representing the sample image 32 may be converted to L*a*b* values ​​by identifying an image entropy-based representation utilizing the sample image features 40 extracted from the sample image data 38 including L*a*b* values.

[0061] The calculated matching sample image 44 is used to match the color and appearance of the target coating 12. The calculated matching sample image 44 may correspond to a paint formula that may match the color and appearance of the target coating 12. The system 10 may include one or more alternative matching sample images 48 associated with the calculated matching sample image 44. The one or more alternative matching sample images 48 may be associated with the calculated matching sample image 44 based on a paint formula, an observed similarity, a calculated similarity, or a combination thereof. In certain embodiments, one or more alternative matching sample images 48 are associated with the calculated matching sample image 44 based on a paint formula. In an embodiment, the calculated matching sample image 44 corresponds to a primary paint formula, and the one or more alternative matching sample images 48 correspond to an alternative paint formula associated with the primary paint formula. The system 10 may include a visual matching sample image 50 that can be selected by a user from the calculated 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.

[0062] Reference Figure 6 and Figure 7 In an embodiment, the electronic 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 that is proximate to the calculated matching sample image 44. In an exemplary embodiment, the display 52 is further configured to display one or more substitute matching sample images 48 associated with the calculated matching sample image 44. In embodiments of the electronic imaging device 16 that include the camera 20, the display 52 may be located opposite the camera 20.

[0063] In an embodiment, the system 10 further includes a user input module 54 configured to select, by a user, a visual matching sample image 50 from among the calculated matching sample image 44 and one or more alternative matching sample images 48 based on a similarity observed by the user to the target coating 12. In an embodiment of the electronic imaging device 16 including a display 52, the user may select the visual matching sample image 50 by a touch input on the display 52.

[0064] In embodiments, system 10 further includes a light source 56 configured to illuminate target coating 12. In embodiments where electronic imaging device 16 includes camera 20, electronic imaging device 16 may include light source 56 and light source 56 may be located proximate to camera 20.

[0065] In an embodiment, the system 10 further includes a dark box (not shown) for isolating the target coating 12 to be imaged from external light, shadows, and reflections. The dark box can be configured to house the electronic imaging device 16 and allow the target coating 12 to be exposed to the camera 20 and the light source 56. The dark box can include a light diffuser (not shown) configured to cooperate with the light source 56 to substantially diffuse the light generated from the light source 56.

[0066] This article also refers to Figure 8 And continue to refer to Figures 1 to 7A method 1100 for matching the color and appearance of a target coating 12 is provided. The method 1100 includes step 1102 of receiving target image data 18 of a target coating 12 by one or more data processors. The target image data 18 is generated by an electronic imaging device 16 and includes target image features 26. The method 1100 also includes step 1104 of retrieving, by the one or more processors, one or more feature extraction algorithms 28' that extract the target image features 26 from the target image data 18. The method 1100 also includes step 1106 of applying the target image features 26 to the one or more feature extraction algorithms 28'. The method 1100 also includes step 1108 of extracting the target image features 26 from the target image data 18 using the one or more feature extraction algorithms 28'.

[0067] The method 1100 also includes step 1110 of retrieving, by one or more data processors, a machine learning model 42 that identifies a calculated matching sample image 44 from a plurality of sample images 32 using the target image features 26. The machine learning model 42 for identifying the calculated matching sample image 44 from the plurality of sample images 32 includes pre-specified matching criteria 46 representing the plurality of sample images 32. The method 1100 also includes step 1112 of applying the target image features 26 to the machine learning model 42. The method 1100 also includes step 1114 of identifying the calculated matching sample image 44 based on substantial satisfaction of one or more of the predetermined matching criteria 46.

[0068] In an embodiment, the method 1100 further includes step 1116 of displaying on the display 52 the calculated matching sample image 44, one or more substitute matching sample images 48 associated with the calculated matching sample image 44, and an image 58 of the target coating 12 proximate to the calculated matching sample image 44 and the one or more substitute matching sample images 48. In an embodiment, the method 1100 further includes step 1118 of selecting, by a user, a visual matching sample image 50 from the calculated matching sample image 44 and the one or more substitute matching sample images 48 based on observed similarity to the target image data 18.

[0069] Reference Fig. 9 And continue to refer to Figures 1 to 8In an embodiment, the method 1100 further includes 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 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 also include step 1124 - extracting sample image features 40 from the plurality of sample images 32 based on one or more feature extraction algorithms 28'. The step 1120 of generating the machine learning model 42 may also include step 1126 - generating a predetermined matching criterion 46 based on the sample image features 40.

[0070] Reference Fig.10 And continue to refer to Figure 1 Fig. 9 In an embodiment, the method 1100 further includes step 1128 —forming a coating composition corresponding to the calculated matching sample image 44 . The method 1100 may further include step 1130 —applying the coating composition to the substrate 14 .

[0071] The methods 1100 and systems 10 disclosed herein may be used for any coated article or substrate 14 that includes 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 equipment, such as televisions, computers, video games, audio and video equipment; recreational equipment, such as bicycles, ski equipment, all-terrain vehicles; home or office furniture, such as tables, filing cabinets; watercraft or vessels, such as boats, yachts, or personal watercraft (PWC); aircraft; buildings; structures, such as bridges; industrial equipment, such as cranes, heavy trucks, or bulldozers; or decorative articles.

[0072] Although at least one exemplary embodiment has been presented in the foregoing detailed description, it should be understood that there are a large number of variations. It should also be understood that one or more exemplary 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 a convenient roadmap for those skilled in the art to implement the exemplary embodiments, and it should be understood that various changes may be made to the functions and arrangements of the elements described in the exemplary embodiments without departing from the scope set forth in the attached claims and their legal equivalents.

Claims

1. A processor-implemented system for matching the color and appearance of a target coating, the system comprising: a storage device for storing instructions for performing the matching of the color and appearance of the target coating; and One or more data processors configured to execute the instructions to: receiving, by one or more data processors, target image data of the target coating, the target image data being generated by an electronic imaging device and comprising target image features; retrieving, by one or more processors, one or more feature extraction algorithms for extracting features of the target image from the target image data; applying the target image data to the one or more feature extraction algorithms; extracting the target image features from the target image data using the one or more feature extraction algorithms, wherein the target image features include representations based on image entropy; retrieving, by one or more data processors, a machine learning model that utilizes the target image feature to identify a calculated matching sample image from a plurality of sample images, wherein the machine learning model includes a predetermined matching criterion representing the plurality of sample images for identifying the calculated matching sample image from the plurality of sample images; applying the target image features to the machine learning model; and identifying the calculated matching sample image based on substantial satisfaction of one or more of the predetermined matching criteria; The calculated matching sample image is used to match the color and appearance of the target coating.

2. The system according to claim 1, wherein: The system also includes an image database including the plurality of sample images, and the one or more data processors are configured to execute the instructions to extract sample image features from the plurality of sample images using the one or more feature extraction algorithms.

3. The system according to claim 2, wherein: The one or more data processors are configured to execute the instructions to generate the predetermined matching criteria based on the sample image features.

4. The system according to claim 3, wherein: The one or more data processors are configured to execute the instructions to train the machine learning model based on the plurality of sample images by generating the predetermined matching criteria based on the sample image features.

5. The system according to claim 1, wherein: The target image data defines RGB values ​​representative of the target coating, and wherein the one or more data processors are configured to execute the instructions to convert the RGB values ​​to L*a*b* values ​​representative of the target coating.

6. The system according to claim 5, wherein: The one or more feature extraction algorithms are configured to identify the image entropy based representation for extracting the target image features from the L*a*b* values ​​of the target image data.

7. The system according to claim 1, wherein: The electronic imaging device is also defined as a mobile device.

8. The system according to claim 1, wherein: The electronic imaging device includes a camera configured to acquire image data of the subject.

9. The system according to claim 1, wherein: The electronic imaging device also includes a display configured to display the calculated matching sample image.

10. The system according to claim 9, wherein: The display is further configured to display an image of the target coating that is proximate to the calculated matching sample image.

11. The system according to claim 10, wherein: The display is further configured to display one or more replacement matching sample images related to the calculated matching sample image.

12. The system according to claim 11, wherein: The calculated matching sample image corresponds to a primary paint formula and the one or more substitute matching sample images correspond to substitute paint formulas related to the primary paint formula.

13. The system of claim 11, further comprising a user input module configured to select, by a user, a visual matching sample image from among the calculated matching sample image and the one or more alternative matching sample images based on a similarity observed by the user to the image of the target coating.

14. The system of claim 1, wherein: The machine learning model includes a random decision forest algorithm, which includes multiple predetermined matching criteria.

15. A method for matching the color and appearance of a target coating, the method comprising: receiving, by one or more data processors, target image data of the target coating, the target image data being generated by an electronic imaging device, the target image data including target image features; retrieving, by one or more processors, one or more feature extraction algorithms for extracting features of the target image from the target image data; applying the target image data to the one or more feature extraction algorithms; extracting the target image features from the target image data using the one or more feature extraction algorithms, wherein the target image features include representations based on image entropy; retrieving, by one or more data processors, a machine learning model that utilizes the target image feature to identify a calculated matching sample image from a plurality of sample images, wherein the machine learning model includes a predetermined matching criterion representing the plurality of sample images for identifying the calculated matching sample image from the plurality of sample images; applying the target image features to the machine learning model; and identifying the calculated matching sample image based on substantial satisfaction of one or more of the predetermined matching criteria; The calculated matching sample image is used to match the color and appearance of the target coating.

16. The method according to claim 15, further comprising the step of generating the machine learning model based on the plurality of sample images, the step of generating the machine learning model comprising: retrieving the plurality of sample images from an image database; extracting sample image features from the plurality of sample images using the one or more feature extraction algorithms; as well as The predetermined matching criterion is generated based on the sample image feature.

17. The method according to claim 15, wherein: The electronic imaging device comprises a display, and wherein the method further comprises the steps of: Displayed on the display: The calculated matching sample image, one or more replacement matching sample images related to the calculated matching sample image, and an image of the target coating proximate the calculated matching sample image and the one or more substitute matching sample images; and A visual matching sample image is selected by a user from among the calculated matching sample images and the one or more alternative matching sample images based on observed similarity to the target image data.

18. The method according to claim 17, wherein: The calculated matching sample image corresponds to a primary paint formula and the one or more substitute matching sample images correspond to substitute paint formulas related to the primary paint formula.

19. The method of claim 15, further comprising forming a coating composition corresponding to the calculated matching sample image.

20. The method of claim 19, further comprising applying the coating composition to a substrate.

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