Systems and methods for matching color and appearance of target coatings

1. A system for matching texture of a target coating comprising:

a storage device for storing instructions;

one or more data processors configured to execute instructions to:

receive a target image of the target coating, wherein the target image comprises target image data;

apply a texture feature extraction analysis process to the target image data to determine target image texture features, wherein the texture feature extraction analysis process includes determining distribution of particle sizes within sub-images of the target image, wherein a data spread statistical measure and a center statistical measure are determined to indicate the distribution of particle sizes;

wherein the data spread statistical measure is a standard deviation statistical measure of the distribution of particle sizes and the center statistical measure is a mean of the distribution of the particle sizes, wherein a kurtosis statistical measure and a skewness statistical measure are determined to describe the distribution of particle sizes;

determine target image texture features for the sub-images, wherein a sub-image includes different particles, and

apply a machine learning model to match texture features present in the target coating using the determined target image texture features, wherein the texture features include visual irregularities associated with the target image.

2. The system of claim 1 , wherein the texture features include one or more in combination of the following characteristics: coarseness, gloss, micro-brilliance, cloudiness, mottle, speckle, sparkle, and glitter.

3. The system of claim 2 , wherein the texture features do not include roughness associated with the target image.

4. The system of claim 1 , wherein the one or more data processors are configured to execute instructions to retrieve the machine learning model to determine the matching of the texture features.

5. The system of claim 4 , wherein the machine-learning model is a convolutional neural network configured to extract and analyze the texture features.

6. The system of claim 1 , wherein color population, the determined texture features of the target coating, and color differences across the target image are used to determine distribution of coarseness across the target coating.

7. The system of claim 1 , wherein the one or more data processors are configured to receive the target image data of the target coating, wherein the target image data corresponds to a plurality of images of the target coating with varying angles of light relative to an imaging device.

8. The system of claim 1 , wherein the one or more data processors are configured to receive the target image data of the target coating, wherein the target image data correlates to a plurality of images of the target coating with varying magnification.

9. The system of claim 1 wherein the target coating is a metallic coating, a pearlescent coating, or a combination thereof.

10. A method for matching texture of a target coating comprising:

receiving, by one or more data processors, a target image of the target coating, wherein the target image comprises target image data;

applying, by the one or more data processors, a texture feature extraction analysis process to the target image data to determine target image texture features, wherein the texture feature extraction analysis process includes determining distribution of particle sizes within sub-images of the target image, wherein a data spread statistical measure and a center statistical measure are determined to indicate the distribution of particle sizes;

wherein the data spread statistical measure is a standard deviation statistical measure of the distribution of particle sizes and the center statistical measure is a mean of the distribution of the particle sizes, wherein a kurtosis statistical measure and a skewness statistical measure are determined to describe the distribution of particle sizes;

determining, by the one or more data processors, target image texture features for the sub-images, wherein a sub-image includes different particles, and

applying, by the one or more data processors, a machine learning model to match texture features present in the target coating using the determined target image texture features, wherein the texture features include visual irregularities associated with the target image.

11. The method of claim 10 , wherein the texture features include one or more in combination of the following characteristics: coarseness, gloss, micro-brilliance, cloudiness, mottle, speckle, sparkle, and glitter.

12. The method of claim 11 , wherein the texture features do not include roughness associated with the target image.

13. The method of claim 10 , wherein the one or more data processors are configured to execute instructions to retrieve the machine learning model to determine the matching of the texture features.

14. The method of claim 13 , wherein the machine-learning model is a convolutional neural network configured to extract and analyze the texture features.

15. The method of claim 10 , wherein color population, the determined texture features of the target coating, and color differences across the target image are used to determine distribution of coarseness across the target coating.

16. The method of claim 10 , wherein the one or more data processors are configured to receive the target image data of the target coating, wherein the target image data corresponds to a plurality of images of the target coating with varying angles of light relative to an imaging device.

17. The method of claim 10 , wherein the one or more data processors are configured to receive the target image data of the target coating, wherein the target image data correlates to a plurality of images of the target coating with varying magnification.

18. The method of claim 10 , wherein the target coating is a metallic coating, a pearlescent coating, or a combination thereof.