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摘要
This paper proposes a seven-characteristic method called Local Feature Similarity (LFS) to recognize weave pattern repeat automatically. The LFS method includes yarn feature acquisition, yarn marking, yarn marking subsequence segmentation, yarn marking subsequence similarity calculation and weave pattern repeat size, which recognizes the repeat size by the texture of the fabric image without considering the color influence. LFS can be applied to the three primary weave patterns and their expansions, such as warp rib, weft rib and panama, and basket weave patterns. Furthermore, six classical adaptive methods are introduced to increase the recognition rate based on the LFS method. As a result, the pre-steps of the recognition of the weave pattern repeat size are improved. Experiments proved the high degree of applicability and robustness of the LFS method. Compared with state-of-the-art methods, the LFS method has the highest recognition rate (99.62%) for the whole sample and excellent performance in average recognition time (6.97 ms). Experiments revealed that the higher recognition rate of the LFS method is helpful for a higher rate of weave pattern repeat recognition and weave pattern recognition (our source code and all experimental results are publicly available: ).
