关键词: 局部二进制模式（LBP）, 特征值, 尺度不变特征变换, 图像匹配

Abstract:

The existing Harris corner extraction algorithm in the image matching method has the defects of low accuracy, weak anti-interference and anti-lighting ability. A robust exact matching algorithm based on the combination of Local Binary Patterns（LBP） and Graph Transformation Matching（GTM） is proposed. Firstly, the improved Harris edge feature detection is used to extract feature points and image blocks are selected as feature regions. Secondly, the improved Center Symmetric Local Binary Patterns（CSLBP） are used to reduce the dimensions of high-dimensional features to generate 24-dimensional feature description. Then, coarse matching of images is realized based on Euclidean distance. Finally, graph variation matching method is used to eliminate error matching to improve the accuracy and robustness of matching. The test results show that the proposed algorithm is effective, it not only has good resistance to scale and rotation changes, but also has strong noise suppression and light resistance. The robust LBP proposed in this paper combines the advantages and disadvantages of traditional feature matching algorithms to make the detection and matching results more accurate. Compared with Harris algorithm and SURF algorithm, the stability and accuracy are significantly improved.

Key words: Local Binary Patterns（LBP）, characteristic value, scale invariant feature transform, image matching