Automotive Coating Color Matching Using Image Feature Extraction
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Solution Overview
Problem
Existing methods for matching the color and appearance of target coatings, such as those used in automotive paints, are cumbersome and expensive, with spectrophotometers being costly and fandecks being difficult to maintain due to the vast number of sample coatings needed.
Innovation Solution
A system and method utilizing an electronic imaging device, data processors, and a machine-learning model to analyze target coating images, extract features, and predict matching coating formulas by comparing with a database of sample images, optimizing color differences through a feature extraction analysis process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If spectrophotometers are used to measure color and appearance attributes, then measurement precision is improved, but device cost and accessibility worsen
Solution Approach 1:
The patent uses digital images as copies of physical coating samples, extracting color and appearance information from image data rather than requiring physical spectrophotometer measurements. This allows the system to work with standard digital cameras and image processing, making the technology more accessible and cost-effective while maintaining measurement capability through computational methods
Solution Approach 2:
The patent replaces the mechanical/optical measurement system (spectrophotometer) with a computational image processing system. By using feature extraction algorithms to analyze digital images and predict coating properties, the system substitutes complex hardware measurement with software-based analysis, reducing device cost and improving accessibility
2Adaptability or versatility
If fandecks with vast number of sample coating layers are used, then adaptability to different coatings is improved, but device complexity and ease of operation worsen
Solution Approach 1:
The patent extracts only the essential color and appearance information from coating samples by analyzing digital images, rather than maintaining physical copies of every possible coating variant. The feature extraction process identifies key characteristics that define coating appearance, allowing the system to work with a reduced set of reference data while maintaining broad adaptability
Solution Approach 2:
The patent transforms the physical coating matching problem into a parameter-based computational problem. By converting coating appearance into quantifiable image features and using algorithms to compare these parameters, the system achieves versatility across different coatings without requiring vast physical sample libraries, thus reducing device complexity
3Productivity
If feature extraction analysis process analyzing target pixels is used, then productivity is improved, but measurement precision requirements worsen
Solution Approach 1:
The patent analyzes only specific target pixels within the image that are most relevant to coating appearance, rather than processing every pixel in the entire image. This selective approach improves computational efficiency and productivity while reducing the overall data processing burden, allowing the system to achieve accurate results with moderate image quality
Data Source
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AI summary
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.