Automated Source Point Selection for Image Retouching
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Solution Overview
Problem
The manual selection of source sampling points for image-retouching tools is time-consuming and challenging due to the proximity of optimal points to defective areas, making it difficult for users to identify and select them accurately.
Innovation Solution
An automated system that determines visual characteristics of defective areas and compares them with candidate replacements to select an optimal source point, using statistical tests and color space conversions to improve the efficiency and quality of image retouching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual selection of source sampling points is used, then user control and precision in selecting source points is maintained, but the process becomes very time-consuming and difficult when optimal points are located close to defective areas
Solution Approach 1:
The system performs self-service by automatically locating source sampling points through algorithmic analysis of visual characteristics, eliminating the need for manual user selection. The computer system independently identifies candidate replacements by comparing visual characteristics of defective areas with surrounding regions, thereby resolving the contradiction between precision and time consumption.
Solution Approach 2:
The manual mechanical process of user selection is replaced with an automated computational system that uses image processing algorithms, color space conversions, and statistical tests to locate source sampling points. This substitution eliminates the time-consuming manual interaction while maintaining or improving selection precision through systematic analysis.
2Measurement precision
If users zoom in on defective areas to identify source sampling points, then selection precision may improve, but the process becomes even more time-consuming and complex
Solution Approach 1:
The complex manual process of zooming and visually searching is replaced with an automated image processing system that analyzes visual characteristics computationally. The system converts images to appropriate color spaces, calculates statistical parameters, and automatically identifies source sampling points without requiring user interaction or manual zooming operations.
Solution Approach 2:
The system changes the parameter space by converting from standard color spaces to color spaces optimized for detecting visual characteristics. This transformation enables the automated system to efficiently identify source sampling points by comparing statistical parameters of different regions, thereby improving accuracy without increasing operational complexity.
3Productivity
If automated systems are used to locate source sampling points, then processing speed and efficiency improve, but the complexity of the system increases
Solution Approach 1:
The automated system segments the source point location task into distinct computational stages: identifying defective areas, analyzing visual characteristics, converting color spaces, comparing candidate regions, and selecting optimal source points. This segmentation organizes the complexity into manageable modules, enabling high-speed automated processing while maintaining system clarity.
Solution Approach 2:
The system employs parameter changes through color space conversions and statistical analysis to automate source point location. By transforming image data into optimized parameter representations and systematically comparing visual characteristics, the system achieves high productivity through algorithmic efficiency rather than manual intervention, managing complexity through mathematical transformations.
Data Source
AI summary
One embodiment of the present invention provides a system that automatically locates a source point within an image for an image-retouching tool. During operation, the system receives an image, wherein the image includes a defective area. Next, the system determines visual characteristics for the defective area. The system then searches around the defective area to identify candidate replacements for the defective area, and also determines visual characteristics for the candidate replacements. Next, the system compares visual characteristics of the defective area with visual characteristics for the candidate replacements to select a candidate replacement to be used as the source point from which pixels can be copied by the image-retouching tool. Note that this system not only improves the usability of image-retouching tools, but can also improve the quality of the result.