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모든 이미지에서 가장자리를 감지하고 시각화합니다. Canny, Sobel, Laplace, Scharr 및 Prewitt 알고리즘 중에서 선택하여 윤곽, 경계 및 구조적 세부 사항을 강조합니다.
Canny, Sobel, Laplacian, Scharr 및 Prewitt 가장자리 감지를 온라인으로 실행하여 경계, 기울기 및 구조적 세부 사항을 시각화하세요.
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Edge detection identifies boundaries in images where brightness changes sharply. It is a fundamental operation in computer vision used for object recognition, image segmentation, and feature extraction.
Canny is a multi-stage algorithm producing clean, thin edges with hysteresis. Sobel uses 3×3+ directional kernels. Scharr is an optimized 3×3 Sobel with better rotational symmetry. Laplace uses the second derivative, detecting edges in all directions. Prewitt uses simpler [-1,0,1] kernels for basic gradient estimation.
In Canny edge detection, the low threshold filters out weak edges and the high threshold identifies strong edges. Pixels between the two thresholds are kept only if connected to strong edges (hysteresis).
dx and dy control the derivative order in the X and Y directions. Setting dx=1,dy=0 detects vertical edges; dx=0,dy=1 detects horizontal edges. At least one must be non-zero.
The kernel size determines the convolution window used for gradient computation. Larger kernels (5×5, 7×7) smooth out noise but may miss fine details. 3×3 is the default for most applications.