A method and system for image superpixel segmentation
A superpixel segmentation and image technology, applied in the field of image processing, can solve the problem that the target area segmentation cannot achieve the desired effect, affect the accuracy of target segmentation, and cannot adjust the calculation strategy, and achieves good edge recall rate and good edge fit. , the effect of preventing edge loss
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[0046] After testing the traditional SLIC algorithm, the inventor found that the main reason why it cannot achieve the desired effect is that the color distance and the space distance are set to have the same weight when performing image segmentation. To illustrate this point, the traditional SLIC algorithm will be introduced below. Operations based on this algorithm include:
[0047] a) Initialize the seed points (that is, the cluster centers), and distribute the seed points evenly in the input image according to the number of superpixels set by initialization. Assuming that the input image has N pixels, the number of initial superpixels is K, then the size of each superpixel is N / K, and the distance between adjacent cluster centers is approximately S=sqrt(N / K);
[0048] b) Reselect the seed point in the small neighborhood around the seed point, that is, take the position with the smallest gradient to avoid the seed point falling on the boundary of the contour;
[0049] c) ...
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