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Keypoint matching using salient regions and GMM in images with weak textures and repetitive patterns

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Abstract

In the keypoint matching task, most standard methods cannot extract uniform and effective keypoints in images with weak textures and repetitive patterns, nor can they design unique feature descriptors for them. In order to bypass the difficulties encountered in matching ambiguous images, we make some efforts from keypoint extraction and keypoint matching respectively. First, a keypoint extractor based on local energy maxima is designed, which can extract stable features in weak texture regions. Second, to guide keypoints matching based on the Gaussian mixture model framework, an adaptive search method to locate salient regions in images without training is introduced. This is accomplished by quantifying the similarity between the center point and the remaining keypoints. Finally, the iterative equations considering the guiding information is derived. These algorithms alleviate the impacts of weak texture and repetitive patterns on keypoints matching. Experiments on test data show that our method can extract stable and reproducible keypoints, in matching, the precision is more than 60% and the recall is more than 80% when images containing description ambiguities.

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Funding

This work was supported by [the National Natural Science Foundation of China] (Grant number [61673129]); [the Development Project of Ship Situational Intelligent Awareness System] (Grant number [MC-201920-X01]).

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Authors and Affiliations

  1. College of Automation, Harbin Engineering University, Harbin, 150001, China

    Qidan Zhu, Feng Wang, Chengtao Cai & Renjie Qiao

  2. Shanghai Aerospace Control Technology Institute, Shanghai, China

    Haiyang Meng

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  1. Qidan Zhu
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  2. Feng Wang
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Correspondence to Feng Wang.

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Zhu, Q., Wang, F., Cai, C. et al. Keypoint matching using salient regions and GMM in images with weak textures and repetitive patterns. Multimed Tools Appl 81, 23237–23257 (2022). https://doi.org/10.1007/s11042-022-12503-0

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