SIFT fast image matching algorithm with local adaptive threshold
Aiming at the problems of complex traditional SIFT matching algorithm,many feature redundancy points,and difficulty in meeting real-time performance,this paper proposes a SIFT fast image matching algorithm with local adaptive threshold.Based on the SIFT algorithm,the proposed method optimizes the construction of Gaussian pyramids,eliminates redundant feature points by reducing the number of pyramid layers to improve the detection efficiency.The threshold in the FAST algorithm is extracted according to the local contrast of the image,so as to achieve high-quality feature point detection.The feature points with strong robustness are screened out for more accurate matching.Secondly,a Gaussian circular window is used to establish a 32-dimensional dimensionality reduction feature vector to improve the operation efficiency of the algorithm.Finally,the feature points are purified according to the geometric consistency between the matching feature point pairs,which effectively reduces the false matching.The experimental results show that the comprehensive performance of the proposed method in terms of matching accuracy and computational efficiency is better than that of SIFT algorithm and other comparative matching algorithms,and the matching accuracy is improved by about 10%and the algorithm execution time is shortened by about 49%compared with the traditional SIFT algorithm.The correct matching rate is above 93%in the case of image scale,rotation and lighting change.
万方数据
维普
