Abstract
Extracting the fuzzy feature of the mobile video image can effectively improve the low illumination image quality. Traditional methods are used to construct fuzzy feature indexes of mobile terminal video images, and the detailed information of video images is divided, but the bidirectional matching of feature points is ignored, which leads to low extraction accuracy. Therefore, this paper proposes a method for extracting fuzzy features of mobile terminal video images based on SURF-based virtual reality technology. First, perform video image grayscale extraction on the input mobile terminal video image, and detect the closed area in the mobile terminal video image as the radiation invariant area of the terminal video image. Secondly, Hessian matrix is used to detect the feature points of the image, and the non-maximum suppression method and interpolation operation are used to find and locate the extreme value points. Then, the main direction of feature points was determined, and SURF description operator was used for matching to obtain initial matching point pairs. Finally, the obtained fuzzy feature one-way matching result of the video image is matched in two directions, the closest distance ratio is used to match the feature points, and the full constraint condition is used to filter out the wrong matching point pairs, thereby completing the mobile terminal video image fuzzy feature extraction. The experimental results show that the proposed algorithm is effective in feature extraction and matching, stability and speed. The misrecognition rate of the algorithm in this paper is 0.101, and the time used is only 0.41 s, which fully meets the real-time requirements.
Highlights
Virtual reality technology can combine processed information with images in real life, giving people an illusion of being in a simulated environment and meeting people's requirements [1-2]
Traditional methods are used to construct fuzzy feature indexes of mobile terminal video images, and the detailed information of video images is divided, but the bidirectional matching of feature points is ignored, which leads to low extraction accuracy
The obtained fuzzy feature one-way matching result of the video image is matched in two directions, the closest distance ratio is used to match the feature points, and the full constraint condition is used to filter out the wrong matching point pairs, thereby completing the mobile terminal video image fuzzy feature extraction
Summary
Virtual reality technology can combine processed information with images in real life, giving people an illusion of being in a simulated environment and meeting people's requirements [1-2]. The calculation speed is slow, there are corner information loss, position shift, and clustering phenomenon On this basis, Seada et al [12-13] improved the Harris matching algorithm, but it cannot adapt to the problem of image scale changes. It can maintain invariance under both image scale and affine transformation, better robustness, and has the problem of high false match rate He et al [16-17] combined with other algorithms to further improve SURF, and achieved good results. The fuzzy feature value of the diffusion tensor is set according to the result of the detailed information division This method has a good denoising effect on video images, but there is a problem that the extraction process takes too long. The obtained fuzzy feature one-way matching result of the video image is matched in two directions, the fuzzy feature extraction of mobile video image is completed
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