Accumulated stability voting: A robust descriptor from descriptors of multiple scales

Tsun Yi Yang, Yen-Yu Lin, Yung Yu Chuang

研究成果: Conference contribution同行評審

20 引文 斯高帕斯(Scopus)

摘要

This paper proposes a novel local descriptor through accumulated stability voting (ASV). The stability of feature dimensions is measured by their differences across scales. To be more robust to noise, the stability is further quantized by thresholding. The principle of maximum entropy is utilized for determining the best thresholds for maximizing discriminant power of the resultant descriptor. Accumulating stability renders a real-valued descriptor and it can be converted into a binary descriptor by an additional thresholding process. The real-valued descriptor attains high matching accuracy while the binary descriptor makes a good compromise between storage and accuracy. Our descriptors are simple yet effective, and easy to implement. In addition, our descriptors require no training. Experiments on popular benchmarks demonstrate the effectiveness of our descriptors and their superiority to the state-of-the-art descriptors.

原文English
主出版物標題Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
發行者IEEE Computer Society
頁面327-335
頁數9
ISBN(電子)9781467388504
DOIs
出版狀態Published - 9 十二月 2016
事件29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 - Las Vegas, United States
持續時間: 26 六月 20161 七月 2016

出版系列

名字Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
2016-December
ISSN(列印)1063-6919

Conference

Conference29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
國家United States
城市Las Vegas
期間26/06/161/07/16

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