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Published on: December 15, 2023
The Bayesian operating point of the Canny edge detector.
1Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield S1 3JD, UK. hegallis@gmail.com
Summary
The Canny edge detector
Area of Science:
- Computer Vision
- Image Processing
- Pattern Recognition
Background:
- The Canny edge detector is a widely used algorithm for edge detection in image processing.
- Understanding the contribution of each stage is crucial for optimizing its performance.
Purpose of the Study:
- To identify the optimal operating point for the Canny edge detector that minimizes Bayes risk.
- To analyze the performance contribution of individual stages within the Canny algorithm.
Main Methods:
- Investigated the sequential stages of the Canny edge detector.
- Evaluated performance based on Bayes risk of misclassification.
- Analyzed the impact of linear filtering, nonmaximal suppression, and hysteresis thresholding.
Main Results:
- The linear filtering stage alone performs poorly, yielding error rates similar to random guessing.
- Edge detection performance is primarily attributed to the postprocessing steps.
- Nonmaximal suppression and hysteresis thresholding are critical for effective edge detection.
Conclusions:
- The Canny edge detector's effectiveness relies heavily on its postprocessing stages.
- Optimizing postprocessing parameters is key to maximizing edge detection accuracy.
- The initial filtering stage requires significant enhancement or replacement for robust performance.
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