Texture feature extraction method fused with visual significance and gray level co-occurrence matrix (GLCM)
A technology of gray-level co-occurrence moments and texture features, applied in the field of computer vision, can solve problems such as slow calculation speed, real-time performance, poor accuracy and robustness, and affecting real-time performance
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[0068] like figure 1 As shown, the present invention is a texture feature extraction method that combines visual saliency and gray co-occurrence moments, and its steps are:
[0069] 1. Initialization;
[0070] Determine the target detection window, basic block, super block size (shown in step ①). The target detection window is determined based on the experience of detecting targets. For example, the detection window for pedestrians is 36*108, the basic block size is 9*12, and the super block size is 18*24. The parameters can be adjusted appropriately according to the size of the actual target;
[0071] If the image is successfully acquired, that is, the image file is successfully read or the camera captures the image successfully (step ②), continue preprocessing including filtering image noise (step ③) to provide more accurate input for the next step, otherwise end ( Step ⑧).
[0072] 2. Feature extraction;
[0073] Calculate the significance factor in units of basic bloc...
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