NDLI: Realtime object matching with robust dominant orientation templates

Content Provider	IEEE Xplore Digital Library
Author	Chaoqun Hong Jianke Zhu Mingli Song Yinting Wang
Copyright Year	2012
Description	Author affiliation: College of Computer Science, Zhejiang University (Jianke Zhu; Mingli Song; Yinting Wang) \|\| College of Computer Science, Xiamen University of Technology (Chaoqun Hong)
Abstract	Most of conventional object matching methods are based on comparing the local features, which are too computational demanding to achieve realtime performance on object detection in videos. Recently, Dominant Orientation Templates (DOT) method was proposed to make online feature detection and comparison feasible. However, it still suffers the problem of fragility due to the noise and partial occlusions. To efficiently tackle these problems, we introduce the similarity map to store the matching scores of individual grids in each sliding window, which is used to further denoise and infer the occlusion map. The promising experimental results demonstrate that proposed approach improves the robustness, which outperforms DOT assuredly.
Starting Page	1152
Ending Page	1155
File Size	293875
Page Count	4
File Format	PDF
ISBN	9781467322164
ISSN	10514651
e-ISBN	9784990644109
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-11-11
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	ICPR Org Committee
Subject Keyword	US Department of Transportation Pattern recognition Robustness Feature extraction Noise Computer vision Conferences
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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