Abstract:
Edge detection is an essential preprocessing step for high-level tasks in image analysis and computer vision. Since image contents vary, it’s hard to set a universal threshold.
This project introduces a real-time adaptive, robust, and effective edge detector. The 2D entropy divides the images into three groups with edge proportion statistics-based reference percentage values. Anchor points were more likely edge pixels than attached points along the gradient direction.
These points were joined into edge segments, each of which was a clean, contiguous, 1-pixel-wide chain of pixels, taking segment direction into account. Experimental results show that the proposed edge detector outperforms edge following methods in accuracy. Real-time post-processing applications can use detection results.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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