Abstract: A system and method include receiving target image data associated with a target coating. A feature extraction analysis process is applied to the target image data to determine a target image feature. The feature extraction analysis process includes dividing the target image into sub-images which contains a plurality of target pixels. A machine learning model identifies one or more types of flakes present in the target coating using target pixel features.

Abstract: Processor implemented systems and methods for matching color and appearance of a target coating are provided herein. A system includes a storage device for storing instructions, and one or more data processors. The data processor(s) are configured to execute instructions to receive a target image of a target coating. The data processor(s) are also configured to apply a feature extraction analysis process that divides the target image into a plurality of target pixels for image analysis.

Abstract: Apparatuses and methods for approximating a 5-angle color difference model are provided, where the 5-angle color difference model utilizes a 5-angle equation. In an exemplary embodiment, an apparatus includes a storage device for storing instructions and one or more processors configured to execute the instructions. The processor(s) are configured to receive 3-angle standard and test color measurements, and enter the 3-angle standard measurement into a neural network empirical model. The neural network empirical model includes a plurality of input nodes, a plurality of hidden nodes connected to the input nodes, and a plurality of output nodes connected to the hidden nodes. The neural network empirical model is configured to output 3-angle tolerance values, and to calculate a 3-angle color difference value using the 5-angle equation for at least one of the 3 color measurement angles using the 3-angle standard and test color measurements and the 3-angle tolerance values.

Abstract: Methods and systems for determining a radar compatible coating are provided. In one example, the method includes obtaining a reflectance measurement of a target coating to characterize a color of the target coating. One or more candidate formulas are generated to determine color matching to the color of the target coating. A corresponding color and a corresponding radar property for each of the one or more candidate formulations is predicted. A radar compatible coating composition that is the same or substantially similar in appearance to the target coating is generated. Generating the radar compatible coating composition is based at least in part on the corresponding color and the corresponding radar property for one of the one or more candidate formulations.

Abstract: 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.

Abstract: Methods and systems for determining a radar compatible coating are provided. In one example, the method includes obtaining a reflectance measurement of a target coating to characterize a color of the target coating. One or more candidate formulas are generated to determine color matching to the color of the target coating. A corresponding color and a corresponding radar property for each of the one or more candidate formulations is predicted. A radar compatible coating composition that is the same or substantially similar in appearance to the target coating is generated. Generating the radar compatible coating composition is based at least in part on the corresponding color and the corresponding radar property for one of the one or more candidate formulations.

Abstract: Processor implemented systems and methods for matching color and appearance of a target coating are provided herein. A system includes a storage device for storing instructions, and one or more data processors. The data processor(s) are configured to execute instructions to receive a target image of a target coating. The data processor(s) are also configured to apply a feature extraction analysis process that divides the target image into a plurality of target pixels for image analysis.

Abstract: 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.

Abstract: A system and method include receiving target image data associated with a target coating. A texture feature extraction analysis process is applied to the target image data to determine a target texture image feature. A machine learning model identifies one or more texture features for matching to a target coating.

Abstract: A system and method include receiving target image data associated with a target coating. A feature extraction analysis process is applied to the target image data to determine a target image feature. The feature extraction analysis process includes dividing the target image into sub-images which contains a plurality of target pixels. A machine learning model identifies one or more types of flakes present in the target coating using target pixel features.

Abstract: Apparatuses and methods for approximating a 5-angle color difference model are provided, where the 5-angle color difference model utilizes a 5-angle equation. In an exemplary embodiment, an apparatus includes a storage device for storing instructions and one or more processors configured to execute the instructions. The processor(s) are configured to receive 3-angle standard and test color measurements, and enter the 3-angle standard measurement into a neural network empirical model. The neural network empirical model includes a plurality of input nodes, a plurality of hidden nodes connected to the input nodes, and a plurality of output nodes connected to the hidden nodes. The neural network empirical model is configured to output 3-angle tolerance values, and to calculate a 3-angle color difference value using the 5-angle equation for at least one of the 3 color measurement angles using the 3-angle standard and test color measurements and the 3-angle tolerance values.

Abstract: Systems and methods for a sample database are provided, where the sample database is for matching a target coating. In one embodiment, the system comprises a sample database 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 that includes a value determined by a sample pixel feature difference between at least two sample pixels.