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检测并可视化任何图像中的边缘。在 Canny、Sobel、Laplace、Scharr 和 Prewitt 算法之间进行选择，以突出显示轮廓、边界和结构细节。
在线运行 Canny、Sobel、Laplacian、Scharr 和 Prewitt 边缘检测，以可视化边界、梯度和结构细节。
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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.