Group enhancement for matching of multi-view image with overlap fuzzy feature

When an object is affected by illumination and noise, traditional methods of image feature group enhancement are mismatched and the accuracy of feature group enhancement is poor. Therefore, a feature group enhancement method for three-dimensional images with overlap fuzzy based on Scale-invariant feature transform (SIFT) algorithm is proposed. Firstly, the scale space of multi-view images is constructed. Next, extreme points are detected and screened to determine the main direction of key points. An image SIFT that is used to describe a sub-feature vector to prevent noise and edge response is generated. Then, overlap fuzzy features of multi-view image are internally and externally normalized. Features are highlighted and the effects of illumination are eliminated by measuring the similarity. Finally, different weights are given to overlap fuzzy features of multi-view images to achieve matching and feature group enhancement. Experimental results show that the proposed method achieves the matching and feature group enhancement process under different illumination conditions and noise environment. Furthermore, the proposed method improves feature group enhancement efficiency and feature group enhancement accuracy.