Precision Color Application Imaging for Boundary Error Detection
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Solution Overview
Problem
Applying color to predefined boundaries, such as makeup on facial features, is challenging, especially for visually impaired individuals, requiring precise application without errors.
Innovation Solution
A system and method using image processing to analyze digital images before and after color application, employing machine learning and color space conversion (CIELAB) to accurately determine color presence and generate heatmaps indicating errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional visual inspection is used for color application, then the system is simple and easy to operate, but the precision and accuracy of color boundary detection is insufficient, especially for visually impaired users
Solution Approach 1:
The patent replaces traditional visual inspection (mechanical/optical system) with an automated image processing system using machine learning algorithms. The system captures images, processes them through ML models to detect color boundaries and application accuracy, substituting human visual inspection with computational analysis to achieve higher precision for color matching and boundary detection
Solution Approach 2:
The patent introduces an image processing system as an intermediary between the color application process and the user. This intermediary captures images, analyzes color application through ML algorithms, and provides feedback, thereby mediating the inspection process to achieve precise boundary detection without requiring direct human visual inspection
2Manufacturing precision
If image processing with machine learning is implemented, then color application precision is improved, but the device complexity and processing time increase
Solution Approach 1:
The patent performs preliminary actions by capturing images at strategic points during the color application process (before and after application). The system prepares reference images of the target area and pre-processes them for comparison, enabling real-time precision feedback without requiring complex post-processing of the entire application sequence
Solution Approach 2:
The patent implements a feedback mechanism where the image processing system analyzes color application results and provides immediate feedback to the user. The ML-based system compares actual color application against the reference image, identifies deviations, and communicates results, creating a closed-loop system that improves precision through iterative correction
3Measurement precision
If comprehensive image processing is performed to eliminate glare and shadows, then measurement accuracy is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary image processing actions to eliminate glare and shadows before color extraction. By pre-processing images to remove lighting artifacts and normalize conditions, the system ensures accurate color measurement without requiring excessive computational resources during the actual color analysis phase
Data Source
AI summary
The present disclosure describes a system and method for using image processing to check color application and to indicate to a user where color application is erroneous. The system and method may include capturing a first digital image of an object before color is applied and a second digital image after color is applied. Then, the first and second digital images may undergo editing processes to prepare the digital images for analysis, analysis to determine colors in the digital images, and the generation of heatmaps in a third digital image showing errors in the application of color.