For this we need two thresholds minVal and maxVal. Hysteresis Threshold: This stage determines which edges are all real edges and which are not. In short, the result is a binary image with “thin edges”. If so, it is considered for the next stage, otherwise it is suppressed (set to zero). So point A is checked against points B and C to see if a local maximum is formed. Points B and C are in the direction of the gradient. Gradient direction is perpendicular to the edge. To do this, at each pixel, it is checked whether the pixel is a local maximum near it in the direction of the gradient. Non-Maximum Suppression, after obtaining the gradient magnitude and direction, performs a full scan of the image to remove any unwanted pixels that may not form an edge. The Laplacian operator has rotation invariance and can detect edges in the XY direction at the same time The Sobel function returns a matrix of floating-point numbers, which needs to be converted to a value between 0-255.
delta: target image pixel increase value, the default is 0. Scale: scaling scale, the default is 1. ksize: Convolution kernel size, the default is 3. dy: derivative order in y direction, such as 1, 2. dx: Derivative order in the x direction, such as 1, 2. depth: The depth of the output image, in order to save the accuracy in calculating the gradient, take CV_64F. The size of the kernel can also be specified by the parameter ksize. We can specify the direction of the derivative to take, vertical or horizontal (via the parameters yorder and xorder respectively). The Sobel operator is a difference operator used to detect where there are significant changes in pixel values, so it is more resistant to noise. The detected floating-point matrix is then converted to an integer. Detecting edges must go through floating-point operations, otherwise many edges will be lost. grayscale images, of course, edges can also be detected for each channel of an RGB image. Usually edges can only be detected on a single channel, i.e. The edge is where the pixel value changes sharply, that is, where the gradient is large. Computer Vision Techniques – Edge Detection. 
Computer Vision Technology – Edge Detection Table of Contents
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