Dynamic Colorspace Selection for Image Edge Detection
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Solution Overview
Problem
Current image processing techniques for edge detection in images are not optimized, as different colorspace models provide varying levels of success in identifying edges, and there is a need to determine the most appropriate colorspace model for effective edge detection, especially in securing sensitive information from being inadvertently disclosed.
Innovation Solution
An apparatus and method that processes image data into patched data using a selected colorspace model, applies a colorspace transform mechanism to transform the data into another model with a higher likelihood of edge detection success, and then applies an edge detection technique to enhance the accuracy of edge identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fixed colorspace model is used for all images, then the processing is simple and fast, but the edge detection accuracy varies and is not optimized for different image types
Solution Approach 1:
The system dynamically selects the appropriate colorspace model based on the characteristics of each image or image group, rather than using a fixed colorspace. This dynamic adaptation allows the edge detection process to optimize for different image types (photographs, drawings, diagrams, etc.), improving detection accuracy while managing complexity through automated classification
Solution Approach 2:
The invention changes the colorspace parameter (RGB, LAB, YUV, etc.) based on the identified image group characteristics. By adjusting this fundamental parameter according to the image type, the system optimizes edge detection performance for different content categories without requiring manual intervention
2Measurement precision
If multiple colorspace models are evaluated for each image, then the edge detection accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The system segments images into distinct groups based on their characteristics (photographs, drawings, diagrams, screenshots). This segmentation allows the application of optimized colorspace selection at the group level rather than evaluating multiple colorspaces for every individual image, reducing processing time while maintaining accuracy benefits
Solution Approach 2:
The invention performs preliminary classification of images into groups before the actual edge detection process. By pre-identifying the image type and selecting the optimal colorspace in advance, the system avoids the time cost of evaluating multiple colorspaces during the critical edge detection phase
Data Source
AI summary
Techniques to improve edge detection in images. Some techniques include logic to process image data into patched image data in accordance with a colorspace model where the patched image data includes color data in a plurality of patches and identify an image group corresponding to the patched image data. The logic may be further configured to select, based upon the image group, a colorspace transform mechanism being operative to transform the image data into transformed image data in accordance with another colorspace model, the other colorspace model having a higher likelihood than the colorspace model at edge detection for the image group. The logic may be further configured to apply the colorspace transform mechanism to the image data to generate the transformed image data in accordance with the other colorspace mode and then, apply an edge detection technique to the transformed image data. Other embodiments are described and claimed.