Abstract
VR panoramic image is an image technology that covers a wide range of scenes. Its imaging range is much larger than that of traditional imaging systems, and it can fully reflect all the information of the imaging space. Although the multi-feature fusion method has been studied for a long time, the methods of multi-feature extraction, fusion and overall optimization have not been widely studied. In view of the shadow problem in VR panoramic images, this paper proposes a multi-feature fusion VR panoramic image shadow elimination algorithm, which uses HSV color features and LBP (Local Binary Pattern) / LSFP (Local Five Similarity Pattern) texture features to obtain shadow detection results and then obtains the final detection results by fusion. The experimental results prove that while ensuring a low missed detection rate, the false detection rate is greatly reduced. The comprehensive evaluation index Avg in this paper is improved by 3.4% compared with the shadow elimination algorithm based on a single feature. This paper proposes an image saliency detection algorithm and image detail enhancement algorithm based on multi-feature fusion. The final saliency map is obtained through linear fusion. Experiments prove that the image detail enhancement algorithm based on multi-feature fusion mentioned in this paper has achieved excellent results. In this paper, the performance of single feature fusion algorithm and multi-feature fusion algorithm are compared. The results show that the accuracy rate of multi-feature fusion algorithm based on HSV, LBP and LSFP is 93.39%, and the effect of multi-feature fusion is better than that of single feature.
Highlights
Virtual Reality (VR) technology is an interactive threedimensional dynamic visual scene that integrates multisource information
Aiming at the common shadow problems in VR panoramic image, this paper proposes a shadow elimination algorithm based on multi-feature fusion, which uses HSV color feature and texture feature to get their shadow detection results, and through the fusion of different shadow detection results to get the final detection results, and eliminate the shadow to get the accurate moving target
This paper proposes an image saliency detection algorithm and image detail enhancement algorithm based on multi-feature fusion, which separates color features and texture features, and obtains the final saliency map through linear fusion
Summary
Virtual Reality (VR) technology is an interactive threedimensional dynamic visual scene that integrates multisource information. Yu: Multi-Feature Fusion Algorithm in VR Panoramic Image Detail Enhancement Processing proposed an image enhancement method based on quaternion guided filter. In [27], [28], the author combined segmentation optimization and multi-feature fusion technology to propose an Object-Based Change Detection (OBCD) method in high-resolution remote sensing images. The working process based on LBP texture features is as follows: select pixels (x, y) of the VR panoramic image area, and calculate the LBP texture feature string pattern results LBPF (x,y) and LBPB(x,y) of the pixel in the current frame and background image to be detected. The shadow elimination algorithm only needs to judge the similarity result of the LFSP operator of the area to be detected and the corresponding background, and does not need specific texture feature values. The size of the LFSP operator is 5 × 5, and the length of its string value is 24
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