Systems and methods for matching color and appearance of target coatings
A system and method include receiving target image data associated with a target coating. A color model and a local color model are used to predict color differences between the target coating and a sample coating. The color model and local color model includes a feature extraction analysis process that determines image features by analyzing target pixel feature differences within the target coating. Performing an optimization routine upon the color differences for determining automotive paint components for spraying a substrate.
1. A system for color matching involving automotive paints, said system 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 color model and a local color model to predict color differences between the target coating and a sample coating,
wherein the input to the color model and local color model include actual color differences between the target coating and the sample coating;
wherein the color model and local color model includes a feature extraction analysis process that determines image features by analyzing target pixel feature differences within the target coating, and
optimize the predicted color differences using a relaxed constraint process;
wherein the optimized color differences determine automotive paint components for spraying a substrate.
2. The system of claim 1 , wherein the color model and the local color model use one or more of L*a*b* color coordinates of the individual target pixels of the target image for determining the color differences.
3. The system of claim 1 , wherein the one or more data processors are configured to execute instructions to retrieve a mathematical model to determine the color differences between the target coating and a sample coating.
4. The system of claim 3 , wherein the mathematical model is a machine-learning model.
5. The system of claim 1 , wherein the predicted color differences are used to determine a coating formula.
6. The system of claim 5 , wherein the one or more data processors are configured to determine a plurality of the coating formulas that correspond to the calculated match sample image.
7. The system of claim 6 , wherein the plurality of coating formulas comprise different grades of coatings.
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 angles of light relative to an imaging device.
9. 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.
10. The system of claim 1 , wherein the target coating is a metallic coating, a pearlescent coating, or a combination of thereof.
11. A method of matching 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 color model and a local color model to predict color differences between the target coating and a sample coating,
wherein the input to the color model and local color model includes actual color differences between the target coating and the sample coating;
wherein the color model and local color model include a feature extraction analysis process that determines image features by analyzing target pixel feature differences within the target coating, and
optimizing, by the one or more data processors, the predicted color differences using a relaxed constraint process;
wherein the optimized color differences determine automotive paint components for spraying a substrate.
12. The method of claim 11 , wherein the color model and the local color model use one or more of L*a*b* color coordinates of the individual target pixels of the target image for determining the color differences.
13. The method of claim 11 , wherein the one or more data processors are configured to execute instructions to retrieve a mathematical model to determine the color differences between the target coating and a sample coating.
14. The method of claim 13 , wherein the mathematical model is a machine-learning model.
15. The method of claim 11 , wherein the predicted color differences are used to determine a coating formula.
16. The method of claim 15 , wherein the one or more data processors are configured to determine a plurality of the coating formulas that correspond to the calculated match sample image.
17. The method of claim 16 , wherein the plurality of coating formulas comprise different grades of coatings.
18. The method of claim 11 , 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 angles of light relative to an imaging device.
19. The method of claim 11 , 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.
20. The method of claim 11 , wherein the target coating is a metallic coating, a pearlescent coating, or a combination of thereof.