Application of SURB Combined with Random Sample Consensus Algorithm in Shoe Uppers Matching

Aiming at the problems of scale change, illumination change and noise interference in the uppers matching, a shoe upper matching detection method based on the speeded-up robust features-object request broker (SURF-ORB) algorithm combined with random sample consensus (RANSAC) algorithm is presented. The feature points of the uppers image arc extracted by SURF. The descriptors arc obtained and the feature points arc described by the ORE algorithm. In order to obtain more accurate matching points, the initial matching is completed by using the Hamming distance, and then by combining the RANSAC algorithm, the mismatching points generated by noise interference and illumination changes arc eliminated. The experimental results show that the algorithm can effectively match and has strong robustness when there arc scale change, illumination change and noise interference in the shoe uppers image.