NDLI: Pruning local feature correspondences using shape context

Content Provider	IEEE Xplore Digital Library
Author	Carneiro, G. Jepson, A.D.
Copyright Year	2004
Description	Author affiliation: Dept. of Comput. Sci., Toronto Univ., Ont., Canada (Carneiro, G.; Jepson, A.D.)
Abstract	We propose a novel approach to improve the distinctiveness of local image features without significantly affecting their robustness with respect to image deformations. Local image features have proven to be successful in computer vision tasks involving partial occlusion, background noise, and various types of image deformations. However, the relatively high number of outliers that have to be rejected from the correspondences set, formed during the search for similar features, still plagues this approach. The task of rejecting outliers is usually based on estimating the global spatial transform suffered by the features in the correspondences set. This presents two problems: (i) it cannot properly deal with non-rigid objects, and (ii) it is sensitive to a high number of outliers. Here, we address these problems by combining typical local features with shape context. A performance evaluation shows that this new semi-local feature generally provides higher distinctiveness and robustness to image deformations, thus potentially increasing the inlier/outlier ratio in the correspondences set. Also, we show that in wide baseline stereo matching, and non-rigid motion applications, the use of the novel semi-local feature not only provides robustness to non-rigid deformations, but also produces a higher inlier/outlier ratio than the standard Hough clustering of the global spatial transform of parameters.
Starting Page	16
Ending Page	19
File Size	864651
Page Count	4
File Format	PDF
ISBN	0769521282
ISSN	10514651
DOI	10.1109/ICPR.2004.1334458
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-08-26
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Application software Computer vision Shape measurement Histograms Noise robustness Background noise Stereo vision Computer science Object recognition Filters
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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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