Walk me through Canny edge detection and why it beats Sobel thresholding
Tests multi-scale edge detection and noise robustness versus raw gradient thresholding. Strong answer lists Gaussian blur, Sobel gradients, non-maximum suppression, double thresholding, hysteresis. Red flag: calling it blurred Sobel without hysteresis or NMS.
Tests deep understanding of structured edge detection pipelines and why raw gradient magnitude produces thick, noisy boundaries. A strong answer walks through five stages: Gaussian smoothing to suppress noise; Sobel filters for gradient magnitude and orientation; non-maximum suppression to thin edges to single-pixel width; double thresholding to classify strong, weak, and non-edges; and hysteresis tracking to preserve weak edges connected to strong ones.
Read the original → Wikipedia: Canny edge detector
- #computer-vision
- #image-processing
- #edge-detection
- #canny
- #sobel
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