A metric based on saliency line feature extraction and connection for matching area selection

Haiyang Hua; Zelin Shi; Yunpeng Liu

doi:10.1117/12.2550346

31 January 2020 A metric based on saliency line feature extraction and connection for matching area selection

Haiyang Hua, Zelin Shi, Yunpeng Liu

Proceedings Volume 11427, Second Target Recognition and Artificial Intelligence Summit Forum; 114270L (2020) https://doi.org/10.1117/12.2550346
Event: Second Target Recognition and Artificial Intelligence Summit Forum, 2019, Changchun, China

Abstract

Selecting a reliability matching area as template is one of the key issues to vision navigation. This paper proposes a metric for matching area selection based on line feature extraction and connection. Firstly, a new line feature is introduced to approximate the reliability information about matching area, which is called saliency line feature. Then, extracting method of these line features is put forward based on monogenic phase congruency model. Secondly, a convex shape descriptor is proposed to represent the spatial distribution characteristic of the line features by connection. Finally, a measure method is defined by merging the quantity and spatial distribution characteristic of the saliency line features, which can guide to select better matching area. The experimental results show that the proposed metric is valid and effective.

Citation Download Citation

Haiyang Hua, Zelin Shi, and Yunpeng Liu "A metric based on saliency line feature extraction and connection for matching area selection", Proc. SPIE 11427, Second Target Recognition and Artificial Intelligence Summit Forum, 114270L (31 January 2020); https://doi.org/10.1117/12.2550346

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