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Proceedings Paper

A shape context based Hausdorff similarity measure in image matching
Author(s): Tian-lei Ma; Yun-peng Liu; Ze-lin Shi; Jian Yin
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Paper Abstract

The traditional Hausdorff measure, which uses Euclidean distance metric (L2 norm) to define the distance between coordinates of any two points, has poor performance in the presence of the rotation and scale change although it is robust to the noise and occlusion. To address the problem, we define a novel similarity function including two parts in this paper. The first part is Hausdorff distance between shapes which is calculated by exploiting shape context that is rotation and scale invariant as the distance metric. The second part is the cost of matching between centroids. Unlike the traditional method, we use the centroid as reference point to obtain its shape context that embodies global information of the shape. Experiment results demonstrate that the function value between shapes is rotation and scale invariant and the matching accuracy of our algorithm is higher than that of previously proposed algorithm on the MEPG-7 database.

Paper Details

Date Published: 11 September 2013
PDF: 7 pages
Proc. SPIE 8907, International Symposium on Photoelectronic Detection and Imaging 2013: Infrared Imaging and Applications, 89070O (11 September 2013); doi: 10.1117/12.2031528
Show Author Affiliations
Tian-lei Ma, Shenyang Institute of Automation (China)
Key Lab. of Optical-Electronics Information Processing (China)
Univ. of Chinese Academy of Sciences (China)
Yun-peng Liu, Shenyang Institute of Automation (China)
Key Lab. of Optical-Electronics Information Processing (China)
Ze-lin Shi, Shenyang Institute of Automation (China)
Key Lab. of Optical-Electronics Information Processing (China)
Jian Yin, The Research Institute on General Development and Argumentation of Equipment of Air Force (China)


Published in SPIE Proceedings Vol. 8907:
International Symposium on Photoelectronic Detection and Imaging 2013: Infrared Imaging and Applications
Haimei Gong; Zelin Shi; Qian Chen; Jin Lu, Editor(s)

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