Related Computer Vision Links
Learn Edges Computer Vision Tutorial, validate concepts with Edges Computer Vision MCQ Questions, and prepare interviews through Edges Computer Vision Interview Questions and Answers.
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