Edge Detection MCQ
Gradients, first and second derivatives, Sobel/Prewitt, non-maximum suppression, double thresholding, and Canny vs Laplacian tradeoffs.
Gradients
Gx, Gy
Sobel
3×3 masks
Canny
NMS + hysteresis
Laplacian
2nd deriv.
Edge detection in Computer Vision
Edges mark intensity discontinuities—often object boundaries. Classical pipelines combine smoothing, gradient estimation, thinning, and linking.
Canny highlights
Gaussian pre-smoothing, gradient magnitude/direction, non-maximum suppression along normal, hysteresis to trace strong edges with weak continuity.
Ideas to remember
First derivatives
Sobel/Prewitt approximate ∂I/∂x and ∂I/∂y; magnitude combines both; direction matters for NMS.
Noise
Derivatives amplify noise—blur σ trades edge localization vs robustness.
Second derivatives
Laplacian zero-crossings locate edges but are sensitive to noise without careful scaling.
Linking
Hysteresis uses high/low thresholds to reduce streaking while preserving weak edge segments attached to strong ones.
Typical Canny flow
Smooth → Gradients → Magnitude/angle → NMS → Hysteresis