Patent Description:
The technical field is directed to coatings technology and more particularly to systems and methods for matching color and appearance of target coatings.

Visualization and selection of coatings having a desired color and appearance play an important role in many applications. For example, paint suppliers must provide thousands of coatings to cover the range of global OEM manufacturers' coatings for all current and recent model vehicles. Providing this large number of different coatings as factory package products adds complexity to paint manufacture and increases inventory costs. Consequently, paint suppliers provide a mixing machine system including typically <NUM> to <NUM> components (e.g., single pigment tints, binders, solvents, additives) with coating formulas for the components that match the range of coatings of vehicles. The mixing machine may reside at a repair facility (i.e., body shop) or a paint distributor and allows a user to obtain the coating having the desired color and appearance by dispensing the components in amounts corresponding to the coating formula. The coating formulas are typically maintained in a database and are distributed to customers via computer software by download or direct connection to internet databases. Each of the coating formulas typically relate to one or more alternate coating formulas to account for variations in coatings due to variations in vehicle production.

Identification of the coating formula most similar to a target coating is complicated by this variation. For example, a particular coating might appear on three vehicle models, produced in two assembly plants with various application equipment, using paint from two OEM paint suppliers, and over a lifetime of five model years. These sources of variation result in significant coating variation over the population of vehicles with that particular coating. The alternate coating formulas provided by the paint supplier are matched to subsets of the color population so that a close match is available for any vehicle that needs repair. Each of the alternate coating formulas can be represented by a color chip in the fan deck which enables the user to select the best matching formula by visual comparison to the vehicle.

Identifying the coating formula most similar to the target coating for a repair is typically accomplished through either the use a spectrophotometer or a fandeck. Spectrophotometers measure one or more color and appearance attributes of the target coating to be repaired. This color and appearance data is then compared with the corresponding data from potential candidate formulas contained in a database. The candidate formula whose color and appearance attributes best match those of the target coating to be repaired is then selected as the coating formula most similar to the target coating. However, spectrophotometers are expensive and not readily available in economy markets.

Alternatively, fandecks include a plurality of sample coating layers on pages or patches within the fandeck. The sample coating layers of the fandeck are then visually compared to the target coating being repaired. The formula associated with the sample coating layer best matching the color and appearance attributes of the target coating to be repaired is then selected as the coating formula most similar to the target coating. However, fandecks are cumbersome to use and difficult to maintain due to the vast number of sample coating layers necessary to account for all coatings on vehicles on the road today.

As such, it is desirable to provide a system and a method for matching color and appearance of a target coating. In addition, 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. <CIT> is directed to a method for matching color and appearance of a target coating of an article, particularly a target coating comprising one or more effect pigments.

Various non-limiting embodiments of a sample database for matching a target coating and systems and methods for the same are provided. In one non-limiting embodiment, the sample database is stored on a storage device. The sample database includes a sample coating formula and a sample image feature with at least one sample coating formula linked to at least one sample image feature. At least one sample image feature includes a spatial micro-color analysis with a value determined by a sample pixel feature difference between at least two sample pixels.

The sample database described above wherein the sample coating formula comprises a sample base coat formula and a sample clear coat formula.

The sample database described above wherein the sample coating formula comprises a sample effect coat formula.

The sample database described above wherein the sample coating formula comprises an effect additive, and wherein the effect additive is selected from a metallic flake effect additive, a pearlescent crystal effect additive, or a combination thereof.

The sample database described above wherein the sample coating is linked to at least two different sample image features that each comprise one or more spatial micro-color analysis.

The sample database described above wherein the spatial micro-color analysis comprises at least the characterizing part of claim <NUM> and one or more of: L*a*b* color coordinates of individual sample pixels of the sample image; Average L*a*b* color coordinates from the individual sample pixels for the sample image; a sparkle area of a black and white image of the sample image; a sparkle intensity of the black and white image of the sample image; a sparkle grade of the black and white image of the sample image; a sparkle color determination of the sample image; a sparkle clustering of the sample image; a sparkle color differences determination within the sample image; a sparkle persistence of the sample image, where the sparkle persistence is a measure of the sparkle as a function of one or more illumination changes during capture of the sample image; a color constancy, at a sample pixel level, with one or more illumination changes during capture of the sample image; a wavelet coefficient determination of the sample image at a sample pixel level; a Fourier coefficient determination of the target image at the target pixel level; an average color of a local area within the sample image, where the local area may be one or more sample pixels, but where the local area is less than a total area of the sample image; a sample pixel count in discrete L*a*b* ranges of the sample image, where the L*a*b* range may be fixed or may be data driven such that the range varies; a maximally populated coordinate determination of cubic bins at the sample pixel level of the sample image, where the cubic bins are based on a <NUM> dimensional coordinate mapping using L*a*b* or RGB values; an overall image color entropy of the sample image; an image entropy of one or more *a*b* planes as a function of a <NUM>rd dimension of the sample image; an image entropy of one or more RGB planes as a function of the <NUM>rd dimension of the sample image; one or more local pixel variation metrics of the sample image; a coarseness of the sample image; one or more vectors of high variance of the sample image of the target coating, where the one or more vectors of high variance are established using principle component analysis; and one or more vectors of high kurtosis of the sample image, where the one or more vectors of high kurtosis are established using independent component analysis.

The sample database described above also including one or more data processors configured to execute instructions to: receive a target image feature; compare the target image feature to the at least one sample image features; and determine a calculated match sample image that substantially satisfies one or more pre-specified matching criteria for the target image feature, wherein the at least one sample image feature is linked to the calculated match sample image in the sample database.

The sample database described above wherein the sample database comprises a plurality of different related match coating formulas that are configured to link with the same at least one sample image feature, wherein the plurality of related match coating formulas comprise a plurality of different grades of the sample coating.

The sample database described above wherein the sample database further comprises a calculated match sample image linked with the sample coating formula for displaying to a user.

The sample database described above also including one or more data processors configured to execute instructions to display the calculated match sample image on a display.

In another non-limiting embodiment, the method of producing a sample database is provided. The method includes preparing a sample coating from a sample coating formula with an effect additive such that an appearance of the sample coating varies from one location to another. A sample image is produced and divided into a plurality of sample pixels. At least one sample image feature is retrieved where that sample image includes a value determined by a sample pixel feature difference between at least two of the sample pixels. The sample coating formula and the at least one sample image feature are saved and linked in the sample database.

The method described above wherein retrieving the at least one sample image features comprise: dividing the sample image into the plurality of sample pixels; determining a sample pixel feature for individual sample pixels of the plurality of sample pixels; determining the sample pixel feature difference between at least two of the individual sample pixels of the sample image; and determining the at least one sample image feature from the sample pixel feature difference.

The method described above wherein determining the sample pixel feature difference comprises determining the sample pixel feature difference for all of the sample pixels of the sample image.

The method described above wherein preparing the sample coating comprises preparing the sample coating wherein the sample coating is selected from a metallic coating, a pearlescent coating, or a combination thereof.

The method described above wherein preparing the sample coating comprises preparing two or more sample coatings that have about the same appearance, such that the two or more sample coatings have the at least one sample image feature that is about the same, and wherein the two or more sample coatings have different sample coating formulas that comprise two or more different grades of the sample coating.

The method described above wherein preparing the sample coating comprises preparing a base coat that comprises the effect additive, and preparing a clear coat overlying the base coat.

The method described above wherein preparing the sample coating comprises preparing a base coat, preparing an effect coat that overlies the base coat, and preparing a clear coat that overlies the effect coat, wherein the effect coat comprises the effect additive.

The method described above wherein retrieving the at least one sample image feature from the sample image data comprises applying a feature extraction analysis process to the sample image.

The method described above wherein applying the feature extraction analysis process to the sample image comprises at least the characterizing part of claim <NUM> and one or more of: determining L*a*b* color coordinates of individual pixels of the sample image; determining sparkle intensity of the black and white image of the sample image; determining sparkle grade of the black and white image of the sample image; determining sparkle color of the sample image; determining sparkle clustering of the sample image; determining sparkle color differences within the sample image; determining sparkle persistence of the sample image, where sparkle persistence is a measure of sparkle as a function of one or more illumination changes during capture of the sample image; determining color constancy, at a sample pixel level, with one or more illumination changes during capture of the sample image; determining wavelet coefficients of the sample image at the sample pixel level; determining Fourier coefficients of the target image at the target pixel level; determining average color of a local area within the sample image, where the local area may be one or more sample pixels, but where the local area is less than a total area of the sample image; determining pixel count in discrete L*a*b* ranges of the sample image, where the L*a*b* range may be fixed or may be data driven such that the range varies; determining maximally populated coordinates of cubic bins at the sample pixel level of the sample image, where the cubic bins are based on a <NUM> dimensional coordinate mapping using L*a*b* or RGB values; determining overall image color entropy of the sample image; determining image entropy of one or more *a*b* planes as a function of a <NUM>rd dimension of the sample image; determining image entropy of one or more RGB planes as a function of the <NUM>rd dimension of the sample image; determining local sample pixel variation metrics of the sample image; determining coarseness of the sample image; determining vectors of high variance of the sample image, where the vectors of high variance are established using principle component analysis; and determining vectors of high kurtosis of the sample image, where the vectors of high kurtosis are established using independent component analysis.

The method described above also including: capturing a calculated match sample image of the sample coating; and saving the calculated match sample image in the sample database, wherein the calculated match sample image is linked to the sample coating formula.

Other advantages of the disclosed subject matter will be readily appreciated, as the same becomes better understood by reference to the following detailed description when considered in connection with the accompanying drawings wherein:.

The following detailed description includes examples and is not intended to limit the invention or the application and uses of the invention. Furthermore, there is no intention to be bound by any theory presented in the preceding background or the following detailed description.

The features and advantages identified in the present disclosure will be more readily understood, by those of ordinary skill in the art, from reading the following detailed description. It is to be appreciated that certain features, which are, for clarity, described above and below in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features that are, for brevity, described in the context of a single embodiment, may also be provided separately or in any sub-combination. In addition, references in the singular may also include the plural (for example, "a" and "an" may refer to one, or one or more) unless the context specifically states otherwise.

The use of numerical values in the various ranges specified in this disclosure, unless expressly indicated otherwise, are stated as approximations as though the minimum and maximum values within the stated ranges were both proceeded by the word "about. " In this manner, slight variations above and below the stated ranges can be used to achieve substantially the same results as values within the ranges. Also, the disclosure of these ranges is intended as a continuous range including every value between the minimum and maximum values.

Techniques and technologies 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 appreciated that the various block components shown in the figures may be realized by any number of hardware, software, and/or firmware components configured to perform the specified functions.

The following description may refer to elements or nodes or features being "coupled" together. As used herein, unless expressly stated otherwise, "coupled" means that one element/node/feature is directly or indirectly joined to (or directly or indirectly communicates with) another element/node/feature, and not necessarily mechanically. Thus, although the drawings may depict one example of an arrangement of elements, additional intervening elements, devices, features, or components may be present in an embodiment of the depicted subject matter. In addition, certain terminology may also be used in the following description for the purpose of reference only, and thus are not intended to be limiting.

Techniques and technologies 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. Such operations, tasks, and functions are sometimes referred to as being computer-executed, computerized, software-implemented, or computer-implemented. In practice, one or more processor devices can carry out the described operations, tasks, and functions by manipulating electrical signals representing data bits at memory locations in the system memory, as well as other processing of signals. The memory locations where data bits are maintained are physical locations that have particular electrical, magnetic, optical, or organic properties corresponding to the data bits. It should be appreciated that the various block components shown in the figures may be realized by any number of hardware, software, and/or firmware components configured to perform the specified functions.

For the sake of brevity, conventional techniques related to graphics and image processing, touchscreen displays, and other functional aspects of certain systems and subsystems (and the individual operating components thereof) may not be described in detail herein. Furthermore, the connecting lines shown in the various figures contained herein are intended to represent an example of functional relationships and/or physical couplings between the various elements. It should be noted that many alternative or additional functional relationships or physical connections may be present in an embodiment of the subject matter.

As used herein, the term "module" refers to any hardware, software, firmware, electronic control component, processing logic, and/or processor device, individually or in any combination, including without limitation: application specific integrated circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or group) and memory that executes one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that provide the described functionality.

As used herein, the term "pigment" or "pigments" refers to a colorant or colorants that produce color or colors. A pigment can be from natural or synthetic sources and can be made of organic or inorganic constituents. Pigments can also include metallic particles or flakes with specific or mixed shapes and dimensions. A pigment is usually not soluble in a coating composition.

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 reflecting pigments. Metallic flakes, such as aluminum flakes, and pearlescent pigments, such as mica-based pigments, are examples of effect pigments.

The term "appearance" can include: (<NUM>) the aspect of visual experience by which a coating is viewed or recognized; and (<NUM>) perception in which the spectral and geometric aspects of a coating is integrated with its illuminating and viewing environment. In general, appearance includes texture, coarseness, sparkle, or other visual effects of a coating, especially when viewed from varying viewing angles and/or with varying illumination conditions. Appearance characteristics or appearance data can include, but not limited to, descriptions or measurement data on texture, metallic effect, pearlescent effect, gloss, distinctness of image, flake appearances and sizes such as texture, coarseness, sparkle, glint and glitter as well as the enhancement of depth perception in the coatings imparted by the flakes, especially produced by metallic flakes, such as aluminum flakes. Appearance characteristics can be obtained by visual inspection or by using an appearance measurement device.

The term "color data" or "color characteristics" of a coating can comprise 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. Color data can further comprise a color code of a vehicle, a color name or description, or a combination thereof. Color data can even further comprise visual aspects of color of the coating, chroma, hue, lightness or darkness. The color data can be obtained by visual inspection, or by using a color measurement device such as a colorimeter, a spectrophotometer, or a goniospectrophotometer. In particular, spectrophotometers obtain color data by determining the wavelength of light reflected by a coating layer. The color data can also comprise descriptive data, such as a name of a color, a color code of a vehicle; a binary, textural or encrypted data file containing descriptive data for one or more colors; a measurement data file, such as those generated by a color measuring device; or an export/import data file generated by a computing device or a color measuring device. Color data can also be generated by an appearance measuring device or a color-appearance dual measuring device.

The term "coating" or "coating composition" can include any coating compositions known to those skilled in the art and can include a two-pack coating composition, also known as "<NUM> coating composition"; a one-pack or <NUM> 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 mono-cure coating composition; a dual-cure coating composition; a lacquer coating composition; a waterborne coating composition or aqueous coating composition; a solvent borne coating composition; or any other coating compositions known to those skilled in the art. The coating composition can be formulated as a primer, a basecoat, or a color coat composition by incorporating desired pigments or effect pigments. The coating composition can also be formulated as a clearcoat composition.

The term "vehicle", "automotive", "automobile" or "automotive vehicle" can include an automobile, such as car, bus, truck, semi-truck, pickup truck, SUV (Sports Utility Vehicle); tractor; motorcycle; trailer; ATV (all-terrain vehicle); heavy duty mover, such as, bulldozer, mobile crane and earth mover; airplanes; boats; ships; and other modes of transport.

The term "formula," "matching formula," or "matching formulation" for a coating composition refers to a collection of information or instruction, based upon that, the 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 a coating composition. In another example, a matching formula includes instructions on how to mix multiple components of a coating composition.

A processor-implemented system <NUM> for matching color and appearance of a target coating <NUM> is provided herein with reference to <FIG>. The target coating <NUM> may be on a substrate <NUM>. The substrate <NUM> may be a vehicle or parts of a vehicle. The substrate <NUM> may also be any coated article including the target coating <NUM>. The target coating <NUM> may include a color coat layer, a clearcoat layer, or a combination of a color coat layer and a clearcoat layer. The color coat layer may be formed from a color coat composition. The clearcoat layer may be formed from a clearcoat coating composition. The target coating <NUM> may be formed from one or more solvent borne coating compositions, one or more waterborne coating compositions, one or more two-pack coating compositions or one or more one-pack coating compositions. The target coating <NUM> 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.

With reference to <FIG> and continued reference to <FIG>, the system <NUM> includes an electronic imaging device <NUM> configured to generate target image data <NUM> of the target coating <NUM>. The electronic imaging device <NUM> may be a device that can capture images under a wide range of electromagnetic wavelengths including visible or invisible wavelengths. The electronic imaging device <NUM> may be further defined as a mobile device. Examples of mobile devices include, but are not limited to, a mobile phone (e.g., a smartphone), a mobile computer (e.g., a tablet or a laptop), a wearable device (e.g., smart watch or headset), or any other type of device known in the art configured to receive the target image data <NUM>. In one embodiment, the mobile device is a smartphone or a tablet.

In embodiments, the electronic imaging device <NUM> includes a camera <NUM> (see <FIG>). The camera <NUM> may be configured to obtain the target image data <NUM>. The camera <NUM> may be configured to capture images having visible wavelengths. The target image data <NUM> may be derived from a target image <NUM> of the target coating <NUM>, such as a still image or a video. In certain embodiments, the target image data <NUM> is derived from a still image. In the embodiment shown in <FIG>, the electronic imaging device <NUM> is shown disposed in the proximity of and spaced from the target coating <NUM>. However, it should be appreciated that the electronic imaging device <NUM> of the embodiment is portable, such that it may be moved to another coating (not shown). In other embodiments (not shown), the electronic imaging device <NUM> may be fixed at a location. In yet other embodiments (not shown), the electronic imaging device <NUM> may be attached to a robotic arm to be moved automatically. In further embodiments (not shown), the electronic imaging device <NUM> may be configured to measure characteristics of multiple surfaces simultaneously.

The system <NUM> further includes a storage device <NUM> for storing instructions for performing the matching of color and appearance of the target coating <NUM>. The storage device <NUM> may store instructions that can be performed by one or more data processors <NUM>. The instructions stored in the storage device <NUM> may include one or more separate programs, each of which includes an ordered listing of executable instructions for implementing logical functions. When the system <NUM> is in operation, the one or more data processors <NUM> are configured to execute the instructions stored within the storage device <NUM>, to communicate data to and from the storage device <NUM>, and to generally control operations of the system <NUM> pursuant to the instructions. In certain embodiments, the storage device <NUM> is associated with (or alternatively included within) the electronic imaging device <NUM>, a server associated with the system <NUM>, a cloud-computing environment associated with the system <NUM>, or combinations thereof.

As introduced above, the system <NUM> further includes the one or more data processors <NUM> configured to execute the instructions. The one or more data processors <NUM> are configured to be communicatively coupled with the electronic imaging device <NUM>. The one or more data processors <NUM> can be any custom made or commercially available processor, a central processing unit (CPU), an auxiliary processor among several processors associated with the electronic imaging device <NUM>, a semiconductor based microprocessor (in the form of a microchip or chip set), or generally any device for executing instructions. The one or more data processors <NUM> may be communicatively coupled with any component of the system <NUM> through wired connections, wireless connections and/or devices, or a combination thereof. Examples of suitable wired connections includes, but are not limited to, hardware couplings, splitters, connectors, cables or wires. Examples of suitable wireless connections and devices include, but not limited to, Wi-Fi device, Bluetooth device, wide area network (WAN) wireless device, Wi-Max device, local area network (LAN) device, <NUM> broadband device, infrared communication device, optical data transfer device, radio transmitter and optionally receiver, wireless phone, wireless phone adaptor card, or any other devices that can transmit signals in a wide range of electromagnetic wavelengths including radio frequency, microwave frequency, visible or invisible wavelengths.

With reference to <FIG>, the one or more data processors <NUM> are configured to execute the instructions to receive, by the one or more data processors <NUM>, target image data <NUM> of the target coating <NUM>. As described above, the target image data <NUM> is generated by the electronic imaging device <NUM>. The target image data <NUM> may define RGB values, L*a*b* values, or a combination thereof, representative of the target coating <NUM>. In certain embodiments, the target image data <NUM> defines the RGB values representative of the target coating <NUM>. The one or more data processors <NUM> may be further configured to execute the instructions to transform the RGB values of the target image data <NUM> to L*a*b* values representative of the target coating <NUM>.

The target image data <NUM> includes target image features <NUM>. The target image features <NUM> may include color and appearance characteristics of the target coating <NUM>, representations of the target image data <NUM>, or a combination thereof. In certain embodiments, the target image features <NUM> may include representations based on image entropy.

The one or more data processors <NUM> are configured to execute the instructions to retrieve, by the one or more data processors <NUM>, one or more feature extraction analysis processes <NUM>' that extract the target image features <NUM> from the target image data <NUM>. In embodiments, the one or more feature extraction analysis processes <NUM>' are configured to identify the representation based on image entropy for extracting the target image features <NUM> from the target image data <NUM>. To this end, the one or more data processors <NUM> may be configured to execute the instructions to identify the representation based on image entropy for extracting the target image features <NUM> from the target image data <NUM>.

Identifying the representation based on image entropy may include determining color image entropy curves for the target image data <NUM>. The target image data <NUM> may be represented in a three-dimensional L*a*b* space with the color entropy curves based on Shannon entropy of each of the a*b* planes, of each of the L*a* planes, of each of the L*b* planes, or combinations thereof. The determination of the color entropy curves may include dividing the three-dimensional L*a*b* space of the target image data <NUM> into a plurality of cubic subspaces, tabulating the cubic spaces having similar characteristics to arrive at a total cubic space count for each characteristic, generating empty image entropy arrays for each of the dimensions of the three-dimensional L*a*b* space, and populating the empty image entropy arrays with the total cubic space counts corresponding to each of the dimensions.

Identifying the representation based on image entropy may also include determining color difference image entropy curves for the target image data <NUM>. The target image data <NUM> may be represented in a three-dimensional L*a*b* space with the three-dimensional L*a*b* space analyzed in relation to an alternative three-dimensional L*a*b* space. The determination of the color difference entropy curves 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.

Identifying the representation based on image entropy may also include determining black and white intensity image entropy from the L* plane of the three-dimensional L*a*b* space of the target image data <NUM>. Identifying the representation based on image entropy may also include determining average L*a*b* values of the target image data <NUM>. Identifying the representation based on image entropy may also include determining L*a*b* values for the center of the most populated cubic subspace.

The one or more data processors <NUM> are also configured to execute the instructions described above to apply the target image data <NUM> to the one or more feature extraction analysis processes <NUM>'. The one or more data processors <NUM> are further configured to execute the instructions described above to extract the target image features <NUM> from the target image data <NUM> utilizing the one or more feature extraction analysis processes <NUM>'.

In an embodiment, the system <NUM> is configured to extract the target image features <NUM> from the target image data <NUM> by identifying the representation based on image entropy of the target image features <NUM>. Identifying the representation based on image entropy may include determining color image entropy curves for the target image data <NUM>, determining color image entropy curves for the target image data <NUM>, determining black and white intensity image entropy from the L* plane of the three-dimensional L*a*b* space of the target image data <NUM>, determining average L*a*b* values of the target image data <NUM>, determining L*a*b* values for the center of the most populated cubic subspace, or combinations thereof.

With reference to <FIG> and <FIG> and continuing to reference to <FIG>, in embodiments, the system <NUM> further includes a sample database <NUM>. The sample database <NUM> may be associated with the electronic imaging device <NUM> or separate from the electronic imaging device <NUM>, such as in a server-based or in a cloud computing environment. It is to be appreciated that the one or more data processors <NUM> are configured to be communicatively coupled with the sample database <NUM>. The sample database <NUM> may include a plurality of sample images <NUM>, such as a first sample image <NUM> as shown in <FIG> and a second sample image <NUM> as shown in <FIG>. In embodiments, each of the plurality of sample images <NUM> is an image of a panel including a sample coating. A variety of sample coatings, defining a set of coating formulas, may be imaged to generate the plurality of sample images <NUM>. The sample images <NUM> may be imaged utilizing one or more different electronic imaging devices <NUM> to account for variations in imaging abilities and performance of each of the electronic imaging devices <NUM>. The plurality of sample images <NUM> may be in any format, such as RAW, JPEG, TIFF, BMP, GIF, PNG, and the like.

The one or more data processors <NUM> may be configured to execute the instructions to receive, by the one or more data processors <NUM>, sample image data <NUM> of the sample images <NUM>. The sample image data <NUM> may be generated by the electronic imaging device <NUM>. The sample image data <NUM> may define RGB values, L*a*b* values, or a combination thereof, representative of the sample images <NUM>. In certain embodiments, the sample image data <NUM> defines the RGB values representative of the sample images <NUM>, such as shown in <FIG> for the first sample image <NUM> and <FIG> for the second sample image <NUM>. The one or more data processors <NUM> may be further configured to execute the instructions to transform the RGB values of the sample image data <NUM> to L*a*b* values representative of the sample images <NUM>. The system <NUM> may be configured to normalize the sample image data <NUM> of the plurality of sample images <NUM> for various electronic imaging devices <NUM> thereby improving performance of the system <NUM>.

The sample image data <NUM> may include sample image features <NUM>. The sample image features <NUM> may include color and appearance characteristics of the sample image <NUM>, representations of the sample image data <NUM>, or a combination thereof. In certain embodiments, the sample image features <NUM> may include representations based on image entropy.

The one or more data processors <NUM> are configured to execute the instructions to retrieve, by the one or more data processors <NUM>, one or more feature extraction analysis processes <NUM>" that extract the sample image features <NUM> from the sample image data <NUM>. In embodiments, the one or more feature extraction analysis processes <NUM>" are configured to identify the representation based on image entropy for extracting the sample image features <NUM> from the sample image data <NUM>. To this end, the one or more data processors <NUM> may be configured to execute the instructions to identify the representation based on image entropy for extracting the sample image features <NUM> from the sample image data <NUM>. It is to be appreciated that the one or more feature extraction analysis processes <NUM>" utilized to extract the sample image features <NUM> may be the same as or different than the one or more feature extraction analysis processes <NUM>' utilized to extract the target image features <NUM>.

In an embodiment, the system <NUM> is configured to extract the sample image features <NUM> from the sample image data <NUM> by identifying the representation based on image entropy of the sample image features <NUM>. Identifying the representation based on image entropy may include determining color image entropy curves for the sample image data <NUM>, determining color image entropy curves for the sample image data <NUM>, determining black and white intensity image entropy from the L* plane of the three-dimensional L*a*b* space of the sample image data <NUM>, determining average L*a*b* values of the sample image data <NUM>, determining L*a*b* values for the center of the most populated cubic subspace, or combinations thereof.

The one or more data processors <NUM> are configured to execute the instructions to retrieve, by one or more data processors, a machine-learning model <NUM> that identifies a calculated match sample image <NUM> from the plurality of sample images <NUM> utilizing the target image features <NUM>. The machine-learning model <NUM> may utilize supervised training, unsupervised training, or a combination thereof. In an embodiment, the machine-learning model <NUM> utilizes 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.

The machine-learning model <NUM> includes pre-specified matching criteria <NUM> representing the plurality of sample images <NUM> for identifying the calculated match sample image <NUM> from the plurality of sample images <NUM>. In embodiments, the pre-specified matching criteria <NUM> are arranged in one or more decision trees. The one or more data processors <NUM> are configured to apply the target image features <NUM> to the machine-learning model <NUM>. In an embodiment, the pre-specified matching criteria <NUM> are included in one or more decision trees with the decisions trees including root nodes, intermediate nodes through various levels, and end nodes. The target image features <NUM> may be processed through the nodes to one or more of the end nodes with each of the end nodes representing one of the plurality of sample images <NUM>.

The one or more data processors <NUM> are also configured to identify the calculated match sample image <NUM> based upon substantially satisfying one or more of the pre-specified matching criteria <NUM>. In embodiments, the phase "substantially satisfying" means that the calculated match sample image <NUM> is identified from the plurality of sample images <NUM> by having the greatest probability for matching the target coating <NUM>. In an embodiment, the machine-learning model <NUM> is based on a random decision forest algorithm including a plurality of decision trees with outcomes of each of the decisions trees, through processing of the target image features <NUM>, being utilized to determine a probably of each of the sample images <NUM> matching the target coating <NUM>. The sample image <NUM> having the greatest probability for matching the target coating <NUM> may be defined as the calculated match sample image <NUM>.

In embodiments, the one or more data processors <NUM> are configured to execute the instructions to generate the pre-specified matching criteria <NUM> of the machine-learning model <NUM> based on the sample image features <NUM>. In certain embodiments, the pre-specified matching criteria <NUM> are generated based on the sample image features <NUM> extracted from the plurality of sample images <NUM>. The one or more data processors <NUM> may be configured to execute the instructions to train the machine-learning model <NUM> based on the plurality of sample images <NUM> by generating the pre-specified matching criteria <NUM> based on the sample image features <NUM>. The machine-learning model <NUM> may be trained at regular intervals (e.g., monthly) based on the plurality of sample images <NUM> included within the sample database <NUM>. As described above, the sample image data <NUM> defining the RGB values representative of the sample images <NUM> may be transformed to L*a*b* values with the sample image features <NUM> extracted from the sample image data <NUM> including L*a*b* values by identifying the representations based on image entropy.

The calculated match sample image <NUM> is utilized for matching color and appearance of the target coating <NUM>. The calculated match sample image <NUM> may correspond to a coating formula potentially matching color and appearance of the target coating <NUM>. The system <NUM> may include one or more alternate match sample images <NUM> related to the calculated match sample image <NUM>. The one or more alternate match sample images <NUM> may relate to the calculated match sample image <NUM> based on coating formula, observed similarity, calculated similarity, or combinations thereof. In certain embodiments, the one or more alternate match sample images <NUM> are related to the calculated match sample image <NUM> based on the coating formula. In embodiments, the calculated match sample image <NUM> corresponds to a primary coating formula and the one or more alternate match sample images <NUM> correspond to alternate coating formulas related to the primary coating formula. The system <NUM> may include a visual match sample image <NUM>, selectable by a user, from the calculated match sample image <NUM> and the one or more alternate match sample images <NUM> based on an observed similarity to the target coating <NUM> by the user.

With reference to <FIG> and <FIG>, in embodiments, the electronic imaging device <NUM> further includes a display <NUM> configured to display the calculated match sample image <NUM>. In certain embodiments, the display <NUM> is further configured to display a target image <NUM> of the target coating <NUM> adjacent the calculated match sample image <NUM>. In an embodiment, the display <NUM> is further configured to display the one or more alternate match sample images <NUM> related to the calculated match sample image <NUM>. In embodiments of the electronic imaging device <NUM> including the camera <NUM>, the display <NUM> may be located opposite of the camera <NUM>.

In embodiments, the system <NUM> further includes a user input module <NUM> configured to select, by a user, the visual match sample image <NUM> from the calculated match sample image <NUM> and the one or more alternate match sample images <NUM> based on an observed similarity to the target coating <NUM> by the user. In embodiments of the electronic imaging device <NUM> including the display <NUM>, the user may select the visual match sample image <NUM> by touch input on the display <NUM>.

In embodiments, the system <NUM> further includes a light source <NUM> configured to illuminate the target coating <NUM>. In embodiments of the electronic imaging device <NUM> including the camera <NUM>, the electronic imaging device <NUM> may include the light source <NUM> and the light source <NUM> may be located adjacent the camera <NUM>.

In embodiments, the system <NUM> further includes a dark box (not shown) for isolating the target coating <NUM> to be imaged from extraneous light, shadows, and reflections. The dark box may be configured to receive the electronic imaging device <NUM> and permit exposure of target coating <NUM> to the camera <NUM> and the light source <NUM>. The dark box may include a light diffuser (not shown) configured to cooperate with the light source <NUM> for sufficiently diffusing the light generated from the light source <NUM>.

A method <NUM> for matching color and appearance of the target coating <NUM> is also provided herein with reference to <FIG> and continuing reference to <FIG>. The method <NUM> includes the step <NUM> of receiving, by one or more data processors, the target image data <NUM> of the target coating <NUM>. The target image data <NUM> is generated by the electronic imaging device <NUM> and includes the target image features <NUM>. The method <NUM> further includes the step <NUM> of retrieving, by one or more processors, one or more feature extraction analysis processes <NUM>' that extracts the target image features <NUM> from the target image data <NUM>. The method <NUM> further includes the step <NUM> of applying the target image features <NUM> to the one or more feature extraction analysis processes <NUM>'. The method <NUM> further includes the step <NUM> of extracting the target image features <NUM> from the target image data <NUM> utilizing the one or more feature extraction analysis processes <NUM>'.

The method <NUM> further includes the step <NUM> of retrieving, by one or more data processors, the machine-learning model <NUM> that identifies the calculated match sample image <NUM> from the plurality of sample images <NUM> utilizing the target image features <NUM>. The machine-learning model <NUM> includes the pre-specified matching criteria <NUM> representing the plurality of sample images <NUM> for identifying the calculated match sample image <NUM> from the plurality of sample image <NUM>. The method <NUM> further includes the step <NUM> of applying the target image features <NUM> to the machine-learning model <NUM>. The method <NUM> further includes the step <NUM> of identifying the calculated match sample image <NUM> based upon substantially satisfying one or more of the pre-specified matching criteria <NUM>.

In embodiments, the method <NUM> further includes the step <NUM> of displaying, on the display <NUM>, the calculated match sample image <NUM>, the one or more alternate match sample images <NUM> related to the calculated match sample image <NUM>, and a target image <NUM> of the target coating <NUM> adjacent the calculated match sample image <NUM> and the one or more alternate match sample images <NUM>. In embodiments, the method <NUM> further includes the step <NUM> of selecting, by the user, the visual match sample image <NUM> from the calculated match sample image <NUM> and the one or more alternate match sample images <NUM> based on the observed similarity to the target image data <NUM>.

With reference to <FIG> and continuing reference to <FIG>, in embodiments, the method <NUM> further includes the step <NUM> of generating the machine-learning model <NUM> based on the plurality of sample images <NUM>. The step <NUM> of generating the machine-learning model <NUM> may include the step <NUM> of retrieving the plurality of sample images <NUM> from the sample database <NUM>. The step <NUM> of generating the machine-learning model <NUM> may further include the step <NUM> of extracting the sample image features <NUM> from the plurality of sample images <NUM> based on one or more feature extraction analysis processes <NUM>'. The step <NUM> of generating the machine-learning model <NUM> may further include the step <NUM> of generating the pre-specified matching criteria <NUM> based on the sample image features <NUM>.

With reference to <FIG> and continuing reference to <FIG>, in embodiments, the method <NUM> further includes the step <NUM> of forming a coating composition corresponding to the calculated match sample image <NUM>. The method <NUM> may further include the step <NUM> of applying the coating composition to the substrate <NUM>.

The method <NUM> and the system <NUM> disclosed herein can be used for any coated article or substrate <NUM>, including the target coating <NUM>. Some examples of such coated articles can include, but not limited to, home appliances, such as refrigerator, washing machine, dishwasher, microwave ovens, cooking and baking ovens; electronic appliances, such as television sets, computers, electronic game sets, audio and video equipment; recreational equipment, such as bicycles, ski equipment, all-terrain vehicles; and home or office furniture, such as tables, file cabinets; water vessels or crafts, such as boats, yachts, or personal watercrafts (PWCs); aircrafts; buildings; structures, such as bridges; industrial equipment, such as cranes, heavy duty trucks, or earth movers; or ornamental articles.

Color matching for effect pigment-based coatings is particularly challenging. Effect coatings include metallic coatings and pearlescent coatings, but may also include other effects such as phosphorescence, fluorescent, etc. Metallic and pearlescent coatings include an effect additive <NUM>, as illustrated in <FIG> with continuing reference to <FIG>. A coating, including a target coating <NUM> and/or a sample coating <NUM> as illustrated, may include several layers overlying the substrate <NUM>. The target coating <NUM> described above may include the same layers as the sample coating <NUM> illustrated in <FIG>, so the description of the layers of the sample coating <NUM> also applies to the target coating <NUM> described above. As used herein, the term "overlying" means "over" such that an intervening layer may lie between the overlying component (the sample coating <NUM> in this example) and the underlying component (the substrate <NUM> in this example,) or "on" such that the overlying component physically contacts the underlying component. Moreover, the term "overlying" means 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 over at least a portion of the underlying component. It is understood that the substrate <NUM> may be moved such that the relative "up" and "down" positions change. Spatially relative terms, such as "top", "bottom", "over" and "under" are made in the context of the orientation of the cross-sectional <FIG>. It is to be understood that spatially relative terms refer to the orientation in <FIG>, so if the substrate <NUM> were to be oriented in another manner the spatially relative terms would still refer to the orientation depicted in <FIG>. Thus, the terms "over" and "under" remain the same even if the substrate <NUM> is twisted, flipped, or otherwise oriented other than as depicted in the figures.

<FIG> illustrates a primer <NUM> overlying the substrate <NUM>, and a base coat <NUM> overlying the primer <NUM>. The primer <NUM> and substrate <NUM> are not considered part of the sample coating <NUM> in this description. An optional effect coat <NUM> overlies the base coat <NUM>, and a clear coat <NUM> overlies the optional effect coat <NUM>. A sample coating formula <NUM> includes a plurality of components <NUM>, where the components <NUM> for the base coat <NUM> may not be the same as the components <NUM> for the optional effect coat <NUM> and/or the clear coat <NUM>. One or both of the base coat <NUM> and the effect coat <NUM> include an effect additive <NUM> as one of the components <NUM>. The effect additive <NUM> is utilized for producing the special effect of the sample coating <NUM>, such as producing a metallized or pearlescent effect. The sample coating formula <NUM> includes a base coat formula <NUM> for the base coat <NUM>, an optional effect coat formula <NUM> for the optional effect coat <NUM>, and a clear coat formula <NUM> for the clear coat <NUM>. The sample coating formula <NUM> may include formulas for other optional layers as well in some embodiments.

A metallic effect is produced when the sample coating <NUM> (or any other coating) includes reflective flakes that are visible. The reflective flakes serve as the effect additive <NUM>. In an embodiment, metal particles in the paint pick up and reflect more incident light than the basic paint colors, giving a coating a varied appearance over a given area. Some of the coating will appear as the color of the base coat, and other portions will reflect light and appear as a sparkle or glimmer. The metallic color is a color that appears to be that of a polished metal. The visual sensation usually associated with metals is a metallic shine that is different than a simple solid color. The metallic color includes a shiny effect that is due to the material's brightness, and that brightness varies with the surface angle relative to a light source. One technique utilized to produce a metallic effect color is to add aluminum flakes (which are an example of an effect additive <NUM>) to a pigmented coating. The aluminum flakes produce a sparkle that varies in size, brightness, and sometimes in color depending on how the flakes are processed. Larger flakes produce a coarser sparkle, and smaller flakes produce a finer sparkle. Different types of flakes may be utilized, such as "silver dollar" flakes that are flat and relatively circular, like a silver dollar coin. Other types of flakes may have jagged edges, like a corn flake. The flakes may also be colored in some embodiments, so the flake produces a colored sparkle.

Adding the aluminum flakes to the base coat <NUM> produces a metallic effect, but if the same type and amount of flakes are added to a translucent effect coat <NUM> that overlies the base coat <NUM>, the coating has a different appearance that is "deeper. " In another embodiment, the base coat <NUM> may include one type and amount of effect additive <NUM>, and the effect coat <NUM> may include a different type and/or amount of effect additive <NUM> to produce yet another appearance. Therefore, many variables can influence the appearance of the metallic color, such as the type of flake, the size of the flake, the coat that includes the flake, the base color, etc. Therefore, it is difficult to match a metallic effect because of the wide variety of factors and appearances that are possible.

A pearlescent coating includes an effect additive <NUM> that selectively reflects, absorbs, and/or transmits visible light, which can result in a colorful appearance that varies based on the flake's structure and morphology. This gives the coating a sparkle as well as a deep color that changes with viewing angle and/or lighting angle. The effect additive <NUM> in a pearlescent coating may be ceramic crystals, and these effect additives <NUM> may be added to the base coat <NUM>, the effect coat <NUM>, or both. Furthermore, the pearlescent effect additive <NUM> may be of varying grades with different sizes, refractive indices, shapes, etc., and the different grades may be used alone or in combination. It is also possible to combine a pearlescent effect additive <NUM> with a metallic effect additive <NUM>, in a wide variety of combinations, and the appearance of the coating will vary with changes in the effect additive concentration, type, positioning, etc. All the different possible variations in the effect additive <NUM> may apply to a single color, so a technique that matches just the color will not be effective at reproducing the appearance of the effect pigment-based coating.

As mentioned above, the effect pigment-based coating has a varying appearance, so different pixels within an image will have different colors, brightness, hue, chroma, or other appearance features, as seen in the illustrations in <FIG>, <FIG>, and <FIG>. Because of this, a color matching protocol that breaks the image into pixels, and then analyzes the image pixel by pixel to determine a pixel difference between two or more pixels, can aid in matching the overall appearance of an effect pigment-based coating. A mathematical model, such as the machine learning model described above, can be utilized to determine features of an image based on differences between pixels within an image. As such, a target image <NUM> can be analyzed pixel by pixel with a mathematical model to produce a target image feature <NUM>, as mentioned above, and this may be combined with other target image features <NUM> such as color (which may be determined for the target image <NUM> as a whole instead of a pixel by pixel determination) or other target image features. The resulting one or more target image features <NUM> may be compared to a sample database <NUM> that has produced similar sample image features <NUM> to find the best match, again as described above. The pixel-by-pixel evaluation can produce a match for effect pigments that is not possible with evaluations based on a target image <NUM> as a whole.

Reference is made to an embodiment illustrated in <FIG>, with continuing reference to <FIG>. An imaging device <NUM> captures a target image <NUM> of a target coating <NUM>, as described above. The imaging device <NUM> is set at an imaging angle <NUM> relative to a surface of the target coating <NUM>, and an illumination source <NUM> is set at an illumination angle <NUM> during capture of the target image <NUM>. The target image <NUM> is divided into a plurality of target pixels <NUM>, where the plurality of target pixels <NUM> vary such that at least one target pixel <NUM> is different than another target pixel <NUM> in appearance.

In a similar manner, with reference to an embodiment illustrated in <FIG> with continuing reference to <FIG>, the image device <NUM> captures a sample image <NUM> of a sample coating <NUM>, again as described above. The image device <NUM> used to capture the sample image <NUM> may be the same as the image device <NUM> used to capture the target image <NUM>, but different imaging devices <NUM> may also be utilized. An illumination source <NUM> is also utilized for capturing the sample image <NUM>, where the imaging device is set at the imaging angle <NUM> relative to a surface of the sample coating <NUM> and the illumination source <NUM> is set at the illumination angle <NUM>, similar to as described for the target image <NUM> in <FIG>. The sample image <NUM> is divided into a plurality of sample pixels <NUM>, where one sample pixel <NUM> has a different appearance than another sample pixel <NUM>. In an embodiment, the sample coating <NUM> includes an effect additive <NUM>, which is illustrated in the illustration of the sample image <NUM> with small dots, and which produces variations in the appearance of the sample pixels <NUM> within the sample image <NUM>. The target and sample coatings <NUM>, <NUM> in <FIG> and <FIG>, respectively, may include a base coat <NUM>, an optional effect coat <NUM>, and a clear coat <NUM>, but variations in the coats that are present are also possible.

A sample database <NUM> is produced for matching a target image <NUM> with a sample coating formula <NUM>, as illustrated in an embodiment in <FIG> with continuing reference to <FIG>. Production of the sample database <NUM> includes producing a sample coating formula <NUM>, where the sample coating formula <NUM> includes an effect additive <NUM>. The sample coating formula <NUM> may include a base coat <NUM> and a clear coat <NUM>, or a base coat <NUM>, an effect coat <NUM>, and a clear coat <NUM>, but other embodiments are also possible. For example, any one of the base coat <NUM>, optional effect coat <NUM>, and clear coat <NUM> may include a plurality of layers, and other coating layers may also be present.

Once the sample coating formula <NUM> is produced, a sample coating <NUM> is produced with the sample coating formula <NUM>. This is typically done by applying the material from the sample coating formula <NUM> onto a substrate <NUM>, such as by spray painting, applying with a brush, dip coating, digital printing, or any other coating technique. In an embodiment, the sample coating <NUM> is formed by spray painting, where the spray painting utilizes recommended spray painting conditions for the grade of coating in the sample coating formula <NUM>. The spray painting conditions may include the type of solvent, the quantity of solvent, the spray gun pressure, the type and/or size of spray gun nozzle used, the distance between the spray gun and the substrate <NUM>, etc. Producing the sample coating <NUM> using the same technique typically utilized by auto body repair shops or others that may need to match a target coating <NUM> may provide a more accurate representation of the finished product that can be expected than if the sample coating <NUM> were applied with another technique. The sample coating formula <NUM> included one or more effect additives <NUM>, so the sample coating <NUM> has a varied appearance where one portion of the sample coating <NUM> appears different than another portion. For example, a sparkle appears different than a matte color.

A sample image <NUM> is then produced with an imaging device <NUM>, such as by photographing the sample coating <NUM>. The sample image <NUM> includes a plurality of sample image data <NUM>, such as RGB values, L*a*b* values, etc. The sample image <NUM> may be one or more still images, or a moving image. In an embodiment, the sample image <NUM> includes a plurality of still images captured with known and specified illumination, imaging angles, distance between the imaging device <NUM> and the sample coating <NUM>, and illumination angles. The sample image <NUM> may also be directed to an essentially flat portion of the sample coating <NUM>, but in some embodiments the sample image may also include one or more images of a sample coating <NUM> with a known curvature.

A feature extraction analysis process <NUM>" may be applied to the sample image data <NUM> to produce a sample image feature <NUM>. The sample image feature <NUM> and the sample coating formula <NUM> are then linked together and saved in the sample database <NUM>. The sample database <NUM> may include a plurality of sample image features <NUM> linked to one sample coating formula <NUM>, and one or more sample images <NUM> may also be linked with the sample coating formula <NUM>. The sample database <NUM> is saved in one or more storage devices <NUM>, which may be the same or different than the storage device(s) <NUM> mentioned above for matching the target coating <NUM>. The storage device <NUM> that saves the sample database <NUM> may be associated with a data processor <NUM>, as described above, to execute instructions for saving and retrieving data from the sample database <NUM>. The process illustrated in <FIG> may be repeated for a wide variety of sample coating formulas <NUM>, so a plurality of sample coating formulas <NUM> are saved in the sample database <NUM>. The sample coating formulas <NUM> may also be configured to about match original equipment coatings provided by vehicle manufacturers, with the intent to match vehicle coatings with a sample coating formula <NUM>. One or more of the sample image(s) <NUM> may be utilized as the calculated match sample image <NUM>, or a different set of parameters may be utilized to capture the calculated match sample image <NUM> from the sample coating <NUM>.

A plurality of sample coating formulas <NUM> that produce about the same sample image <NUM> may be produced, where the sample coating formulas <NUM> are different from each other and include different grades of a coating. Some vehicle repair shops tend to utilize one grade of coating, and the grade of coating may vary from one vehicle repair shop to the next. A sample database <NUM> that includes a matching sample coating formula <NUM> that utilizes the same grade of coating that the vehicle repair shop is familiar with may improve the results because of the skill and familiarity the vehicle repair shop has with a particular grade of coating. In this manner, the same sample database <NUM> can be utilized by different vehicle repair shops (or other users of the sample database <NUM>) that typically use different grades of a coating.

The feature extraction analysis process <NUM>" described above for <FIG> is shown in greater detail in an embodiment in <FIG>, with continuing reference to <FIG>. The sample image <NUM> is divided into a plurality of sample pixels <NUM>. Each sample pixel <NUM> has sample pixel image data, and the sample pixel image data is utilized to determine a sample pixel feature <NUM> for the sample pixel <NUM>. The sample pixel feature <NUM> may be different than an overall sample feature <NUM>, because the sample pixel <NUM> may have a different appearance than the overall sample image <NUM>. The sample pixel feature <NUM> may be a determination of the RGB values for that sample pixel <NUM>, or the L*a*b* values for that sample pixel <NUM>, or other appearance attributes for that sample pixel <NUM>. In an embodiment, a sample pixel feature <NUM> is determined for all of the sample pixels <NUM> of the sample image <NUM>, but a sample pixel feature <NUM> may be determined for only a subset of the sample pixels <NUM> of the sample image <NUM> in alternate embodiments. In all embodiments, a sample pixel feature <NUM> is determined for a plurality of the sample pixels <NUM>, where that sample pixel feature <NUM> varies for at least some of the sample pixels <NUM>. A sample pixel feature difference <NUM> is then determined for the sample pixels <NUM>. The sample image feature <NUM> is then determined from the sample pixel feature difference <NUM>.

An embodiment is illustrated in <FIG>, with continuing reference to <FIG>, where a three-dimensional coordinate system <NUM> is utilized. The three-dimensional coordinate system <NUM> may represent the RGB color system, where one axis of the three-dimensional coordinate system <NUM> is the R value, another axis is the G value, and the third axis is the B value. Alternatively, the three-dimensional coordinate system <NUM> may represent the L*a*b* color system, where one axis is the L* value, another axis is the a* value, and the third axis is the b* value. The three-dimensional coordinate system <NUM> may represent other axis in alternate embodiments, and in some embodiments a three-dimensional coordinate system <NUM> may not be utilized. The sample pixel features <NUM> may then be plotted in the three-dimensional coordinate system <NUM>, and the number of sample pixels <NUM> that fall within each block of the three-dimensional coordinate system <NUM> may be tallied. In the hypothetical illustration in <FIG>, the first block nearest the <NUM>-<NUM>-<NUM> coordinate has a tally of <NUM>, the block directly above it has a tally of <NUM>, and the block directly above that has a tally of <NUM>. Alternate techniques may be utilized to determine the sample pixel feature difference <NUM>, and a plurality of techniques may be utilized to determine a plurality of sample pixel feature differences <NUM> for one sample image <NUM> in a variety of manners.

A sample image feature <NUM> may then be determined from the sample pixel feature differences <NUM>, as illustrated in <FIG> with continuing reference to <FIG> and <FIG>. Determining a sample image feature <NUM> from a plurality of sample pixel feature differences <NUM> is referred to herein as a spatial micro-color analysis, because the sample image feature <NUM> is based on color or appearance changes in difference spaces within a sample image <NUM>. There may be plurality of sample image features <NUM> determined for one single sample image <NUM>, and some of those sample image features <NUM> may be based on spatial micro-color analysis, and others may not. However, in this description, at least one of the sample image features <NUM> is based on spatial micro-color analysis. As such, the sample database <NUM> includes at least one sample image feature <NUM> that is based on spatial micro-color analysis. The sample image data <NUM> block at the top of <FIG> and the sample image feature <NUM> block at the bottom of <FIG> show an embodiment of the steps utilized between the comparably labeled blocks in <FIG>. A calculated match sample image <NUM> may also be saved in the sample database <NUM>, where the calculated match sample image <NUM> may be utilized for display to a user as described above. The user can then determine whether the calculated match sample image <NUM> is an acceptable match for the target coating <NUM> based on a visual analysis. The calculated match sample image <NUM> may be captured with the imaging device <NUM> under conditions that render the calculated match sample image <NUM> acceptable for visual analysis.

Reference is made to an embodiment illustrated in <FIG>, with continuing reference to <FIG>. <FIG> illustrates an embodiment for utilizing spatial micro-color analysis on the target image <NUM>, as exemplified in the feature extraction analysis process <NUM>' block in <FIG>. As such, the target image data <NUM> block at the top of <FIG> and the target image feature <NUM> block third from the bottom of <FIG> are taken from the comparably named blocks in <FIG>. <FIG> also illustrates an embodiment where the mathematical model <NUM> utilized to compare the target image feature <NUM> with the sample image feature <NUM> is something other than a machine learning model <NUM>. However, it is to be understood that a machine learning model <NUM> may still be utilized in some embodiments. The sample image features <NUM> illustrated in <FIG> and <FIG> are derived from the sample database <NUM>, as described above.

The target image <NUM> is divided into a plurality of target pixels <NUM>, as illustrated in <FIG> and <FIG>. Each target pixel <NUM> includes a target pixel image data, where the target pixel image data is at least a part of the target image data <NUM>. A target pixel feature <NUM> is determined from the target pixels <NUM>, where each of the target pixels <NUM> has at least some of the target image data <NUM> and at least some of that target image data <NUM> for the different target pixels <NUM> varies, as described above for the sample pixel features <NUM>. A target pixel feature difference <NUM> is then determined from the target pixel features <NUM>, and a target image feature <NUM> is then determined from the target pixel feature differences <NUM>, again as described above for the sample pixel feature <NUM>. As such, because at least some of the target pixels <NUM> have different target image data <NUM>, at least one of the target image features <NUM> is determined by spatial micro-color analysis. The target image features <NUM> for a single target image <NUM> may include one or more target image features <NUM> that are determined by spatial micro-color analysis, and may also include one or more target image features <NUM> that are not based on spatial micro-color analysis. Pre-specified matching criteria <NUM> are then utilized to determine a calculated match sample image <NUM>, as described above.

A wide variety of spatial micro-color analysis mathematical techniques may be utilized to determine the target image feature <NUM> and/or the sample image feature <NUM>, and the same techniques may be utilized in some embodiments to facilitate matching. A partial listing of spatial micro-color analysis mathematical techniques is listed below, where the generic terms pixel means either a target or sample pixel <NUM>, <NUM>; and image means either a target or sample image <NUM>, <NUM>. According to the invention spatial micro-color analysis comprises the parameters defined in claims <NUM> and <NUM>. These parameters and further examples of spatial micro-color analysis mathematical techniques include, but are not limited to: determining L*a*b* color coordinates of individual pixels of the image; determining average L*a*b* color coordinates from individual pixels for a total image of the image; determining sparkle area of a black and white image of the image; determining sparkle intensity of the black and white image of the image; determining sparkle grade of the black and white image of the image; determining sparkle color of the image; determining sparkle clustering of the image; determining sparkle color differences within the image; determining sparkle persistence of the image, where sparkle persistence is a measure of the sparkle as a function of one or more illumination changes during capture of the image; determining color constancy, at a pixel level, with one or more illumination changes during capture of the image; determining wavelet coefficients of the image at the pixel level; determining Fourier coefficients of the target image at the target pixel level; determining average color of local areas within the image, where the local area may be one or more pixels, but where the local area is less than a total area of the image; determining pixel count in discrete L*a*b* ranges of the image, where the L*a*b* range may be fixed or may be data driven such that the range varies; determining maximally populated coordinates of cubic bins at the pixel level of the image, where the cubic bins are based on a <NUM> dimensional coordinate mapping using L*a*b* or RGB values; determining overall image color entropy of the image; determining image entropy of one or more L*a*b* planes as a function of a <NUM>rd dimension of the image; determining image entropy of one or more of the RGB planes as a function of the <NUM>rd dimension of the image; determining local pixel variation metrics of the image; determining coarseness of the image; determining vectors of high variance of the image, where the vectors of high variance are established using principle component analysis; and determining vectors of high kurtosis of the image, where the vectors of high kurtosis are established using independent component analysis.

Determining L*a*b* color coordinates of individual pixels of the target/sample image includes breaking the image into pixels, and then determining the L*a*b* color coordinates for a plurality (or all in some embodiments) of the pixels. Determining average L*a*b* color coordinates from individual pixels for a total image of the image means averaging the L*a*b* samples for each of the pixels within the image. Determining sparkle area of a black and white image of the image means rendering or obtaining the image in black and white, and then determining how many pixels include a sparkle and a total number of the pixels within a given area. The given area may be the entire image, or a subset of the image. The sparkle area is then determined by dividing the number of pixels in the given area that include a sparkle by the total number of the pixels in that given area. Determining sparkle intensity of the black and white image of the image means determining the average brightness of the pixels that include a sparkle within a given area.

Determining sparkle grade of the black and white image means determining a value derived from the sparkle area and sparkle intensity that describes the visual perception of the sparkle phenomena. Determining sparkle color of the image means determining the location of a sparkle, and then determining the color of that sparkle. Determining sparkle clustering of the image means determining the various colors of sparkle that are present in an image using clustering or distribution fitting algorithms. Determining sparkle color differences within the image means determining the color of the sparkles in the image, as mentioned above, and then determining the variation or differences in that color in the sparkles. Determining sparkle persistence of the image means determining if a sparkle remains within a given pixel (and if the sparkle has a change in brightness) when the illumination changes during capture of the image, and where the imaging angle <NUM> and the illumination angle <NUM> remain the same. Determining color constancy, at a pixel level, means determining if a color of a pixel remains the same within a given pixel when the illumination changes during capture of the image, and where the imaging angle <NUM> and the illumination angle <NUM> remain the same. At least two different images are needed to determine sparkle persistence and color constancy.

Determining the Fourier coefficients of the image at the pixel level means determining coefficients associated with the image after decomposing the image into sinusoid components of different frequency using the 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 area of the image. Determining 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 a wavelet using the discrete or continuous wavelet transform, where the coefficients describe the image content associated with the relevantly shifted and scaled version of the wavelet. A wavelet is a function that tends towards zero at the extrema with a local area containing an oscillation. This can be determined for a given number of pixels within a given area of the image. Determining average color of local areas within the image means determining the average color of one or more pixels within a sample area that is less than the total area of the image. Determining the pixel count in discrete L*a*b* ranges of the image means determining the pixel count within a given area of a three-dimensional coordinate system <NUM> using the L*a*b* values as the axes, similar to the illustration in <FIG>. The size of the given area within the three-dimensional coordinate system <NUM> may be fixed, or the size may vary depending on the count values generated. Determining maximally populated coordinates of cubic bins at the pixel level of the image, where the cubic bins are based on a three-dimensional coordinate system <NUM> using L*a*b* or RGB values as the axes, means determining the blocks with the highest pixel counts as illustrated in <FIG>. Set values, percentage values, or other metrics may be used to determine the value that designates a block as being "maximally" populated.

Determining overall image color entropy of the image means determining the overall color entropy of the image based on variations within the pixels. Color entropy may be Shannon entropy or other types of entropy calculations. Determining image entropy of one or more of the L*a*b* planes or RGB planes as a function of the <NUM>rd dimension of the image means selecting a plane within the three-dimensional coordinate system <NUM> as illustrated in <FIG>, and then determining the entropy based on the values along that plane. The entropy can be Shannon entropy or other types of entropy calculations, as mentioned above. Determining local pixel variation metrics of the image means selecting essentially any pixel feature and then determining the variation of that pixel feature within the image. Determining coarseness of the image means determining the impression of coarseness of an image based on shadowing or other features that suggest coarseness. Determining vectors of high variance of the image means determining vectors based on a vector origin at a coordinate origin, where the vectors of high variance are established using principle component analysis. The vectors of high variance can be considered target or sample image features <NUM>, <NUM>, or pixel data from the image can be projected onto the vectors to derive new target or sample image features <NUM>, <NUM>. Determining vectors of high kurtosis of the image again means determining vectors based on a vector origin at the coordinate origin, where the vectors of high kurtosis are established using independent component analysis. The vectors of high kurtosis can be considered target or sample image features <NUM>, <NUM>, or pixel data from the image can be projected onto the vectors to derive new target or sample image features <NUM>, <NUM>.

A method of matching a target coating <NUM> is provided in another embodiment, as illustrated in <FIG> with continuing reference to <FIG>. The method includes obtaining <NUM> a target image <NUM> of a target coating <NUM>, where the target coating <NUM> is an effect pigment-based coating that includes an effect additive <NUM>. The method further includes applying <NUM> a feature extraction analysis process <NUM>' to the target image <NUM>, where the feature extraction analysis process <NUM>' includes dividing <NUM> the target image <NUM> into a plurality of target pixels <NUM> that include target pixel image data; determining <NUM> a target pixel feature <NUM> for the individual target pixels of the plurality of target pixels <NUM>; determining <NUM> a target pixel feature difference <NUM> between the individual target pixels <NUM>; determining <NUM> a target image feature <NUM> from the target pixel feature difference <NUM>; and calculating <NUM> the calculated match sample image <NUM> with the target image feature <NUM> based upon substantially satisfying one or more pre-specified matching criteria.

A method of producing a sample database <NUM> is also provided, as illustrated in <FIG> with continuing reference to <FIG>. The method includes <NUM> preparing a sample coating <NUM> from a sample coating formula <NUM> that includes an effective additive <NUM> such that the appearance of the sample coating <NUM> varies from one location to another. The method also includes imaging <NUM> the sample coating to produce a sample image <NUM> that comprises sample image data, where the sample image <NUM> is divided into a plurality of sample pixels <NUM> that each comprise sample pixel image data. The next step is retrieving <NUM> one or more sample image features <NUM> from the sample image data, where at least one of the sample image features <NUM> comprises a spatial micro-color analysis that includes a value determined by a sample pixel feature difference <NUM> between at least two of the sample pixels <NUM>. Another step is saving <NUM> the sample coating formula <NUM> and one or more sample image features <NUM> in the sample database <NUM>, where the sample coating formula <NUM> is linked to the one or more sample image features <NUM>.

Reference is made to <FIG>. Once the calculated match sample image <NUM> is obtained, it may be approved by an operator. At that time, a repair coating <NUM> may be prepared using the sample coating formula <NUM> that corresponds to the calculated match sample image <NUM>. In an exemplary embodiment, the sample coating formula <NUM> includes an effect additive <NUM> and one or more other components <NUM>. The repair coating <NUM> may be applied to a substrate <NUM>, such as a vehicle needing repairs, using one or more of a wide variety of techniques. In an exemplary embodiment, the repair coating <NUM> is applied to the substrate <NUM> utilizing digital printer <NUM> and a digital printing technique, as illustrated, but in alternate embodiments the repair coating <NUM> may be applied to the substrate <NUM> by other techniques including, but not limited to, spray painting, applying with a brush, and/or dip coating.

Claim 1:
A system (<NUM>) for matching a target coating comprising:
a storage device for storing information, wherein a sample database is stored on the storage device, and wherein the sample database comprises at least one sample coating formula and at least one sample image feature wherein
the at least one sample coating formula is linked to the at least one sample image feature, wherein each of the at least one sample coating formulas include a list of components and component quantities for a sample coating, and wherein the at least one sample image feature has been measured from the sample coating;
wherein the at least one sample image feature comprises a spatial micro-color analysis, wherein the spatial micro color analysis comprises a value determined by a sample pixel feature difference between a least two sample pixels of a sample image, wherein the sample image is an image of the sample coating, and wherein the sample image comprises a plurality of sample pixels;
characterized in that the spatial micro-color analysis comprises one or more of: a sparkle area of a black and white image of the sample image; a sparkle intensity of the black and white image of the sample image; a sparkle grade of the black and white image of the sample image; a sparkle color determination of the sample image; a sparkle clustering of the sample image; a sparkle color differences determination within the sample image; a wavelet coefficient determination of the sample image at a sample pixel level; an overall image color entropy of the sample image; an image entropy of one or more *a*b* planes as a function of a <NUM>rd dimension of the sample image; and, an image entropy of one or more RGB planes as a function of the <NUM>rd dimension of the sample image.