| Summary: | According to the characteristics of exaggeration and deformation of paper cuttings patterns, this paper adopts machine vision to classify and recognize paper cuttings patterns and extract features. Based on the analysis of the characteristics acquired above, the nearest neighbor classifier is used for pattern recognition. Then, the normalized invariant moments are used to train the BP neural network, and a new neural network structure is obtained by combining hollow convolution and feature fusion modules to train the model to learn features. During the process of feature extraction, the improved SIFT algorithm is applied to improve the efficiency of the algorithm through dimensionality reduction, achieving the final feature matching. The experimental test uses a large number of paper cuttings pattern examples, and adopt Libsvm to process actual features. The results show that the improved algorithm can effectively remove noise interference in paper cuttings pattern matching, which greatly improves the accuracy, and the time complexity is better than traditional SIFT algorithm. © 2024 The Authors.
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