Abstract: A method and system for matching image features is applied to mine machine vision. The method includes de-noising an image, performing super-pixel segmentation to obtain a plurality of image blocks, calculating the information entropy of each image block to obtain an image block with information entropy greater than a first preset threshold, extracting feature points of the image block, to obtain the feature point set of the image, using a wavelet method to describe the feature points in the feature point set to obtain a feature point descriptor set, and matching feature points in the feature point set with the feature points of a target image.

Abstract: A method and a system for video processing, including: determining a target bounding box from an image frame in a video; determining a classification result of a subject in the target bounding box by processing the target bounding box using a recognition model; and determining one or more target image frames to be saved in the video based on the classification result.

Abstract: A method and system for matching image features is applied to mine machine vision. The method includes de-noising an image, performing super-pixel segmentation to obtain a plurality of image blocks, calculating the information entropy of each image block to obtain an image block with information entropy greater than a first preset threshold, extracting feature points of the image block, to obtain the feature point set of the image, using a wavelet method to describe the feature points in the feature point set to obtain a feature point descriptor set, and matching feature points in the feature point set with the feature points of a target image.

Abstract: A method for tracking a moving target based on an optical flow method, including, providing video images, and implementing pre-processing of the images to generate pre-processed images; implementing edge-detection of the pre-processed images and using an optical flow method to extract target information from the pre-processed images, and on the basis of a combination of the edge-detection information and the extracted target information, generating a complete moving target; using an optical flow method to perform estimation analysis of the moving target and using a forward-backward error algorithm based on feature point trace to eliminate light-generated false matching points; and creating a template image and implementing template image matching to track the moving target. The method and system for tracking a moving target based on an optical flow method have the advantages of accurate and complete extraction and the ability to implement stable tracking over a long period of time.

Abstract: A method for tracking a moving target based on an optical flow method, including, providing video images, and implementing pre-processing of the images to generate pre-processed images; implementing edge-detection of the pre-processed images and using an optical flow method to extract target information from the pre-processed images, and on the basis of a combination of the edge-detection information and the extracted target information, generating a complete moving target; using an optical flow method to perform estimation analysis of the moving target and using a forward-backward error algorithm based on feature point trace to eliminate light-generated false matching points; and creating a template image and implementing template image matching to track the moving target. The method and system for tracking a moving target based on an optical flow method have the advantages of accurate and complete extraction and the ability to implement stable tracking over a long period of time.

Abstract: The invention relates to image processing technology field, and discloses a video image denoising and enhancing method based on random spray retinex, including: structuring spray pixel sets, and tuning parameters related to the random spray retinex based on the spray pixel sets, wherein the parameters include quantity of the spray pixel sets and quantity of pixels; processing video images with random spray retinex based on tuned parameters; denoising the video images processed by the random spray retinex via low pass filters and blur channels to get a brightness variation calculating formula; obtaining a brightness calculating formula of output images, combined with the brightness variation calculating formula, and calculating brightness variations of three channels via the brightness calculating formula to get local brightness estimating vectors; and fusing the three channels based on the local brightness estimating vectors to get denoised and enhanced video images.

Abstract: The invention relates to image processing technology field, and discloses a video image denoising and enhancing method based on random spray retinex, including: structuring spray pixel sets, and tuning parameters related to the random spray retinex based on the spray pixel sets, wherein the parameters include quantity of the spray pixel sets and quantity of pixels; processing video images with random spray retinex based on tuned parameters; denoising the video images processed by the random spray retinex via low pass filters and blur channels to get a brightness variation calculating formula; obtaining a brightness calculating, formula of output images, combined with the brightness variation calculating formula, and calculating brightness variations of three channels via the brightness calculating formula to get local brightness estimating vectors; and fusing the three channels based on the local brightness estimating vectors to get denoised and enhanced video images.