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Paper Abstract and Keywords
Presentation 2017-02-18 11:15
Accurate Feature Point Tracking using Local Invariant Features for Omnidirectional Image Sequence
Hiroshi Tada, Koichi Ichige (YNU)
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper, we propose a highly accurate feature point tracking method for omnidirectional image sequence. It is often difficult to extract and track feature points because of nonlinear distortion of omnidirectional images. We have already proposed a method of tracking feature points after transforming the vicinity of a feature point into perspective projection, but sometimes tracking performance was not accurate. In the proposed method, feature points are extracted from each face of regular icosahedron on which omnidirectional image is projected. Furthermore we introduce ASIFT into the patch, which enables robust tracking of deformation. Performance of the proposed method is evaluated through experiments.
Keyword (in Japanese) (See Japanese page) 
(in English) Feature Point Tracking / Omnidirectional Image / Affine Invariant Local Features / / / / /  
Reference Info. ITE Tech. Rep., vol. 41, no. 4, ME2017-4, pp. 13-16, Feb. 2017.
Paper # ME2017-4 
Date of Issue 2017-02-11 (ME) 
ISSN Print edition: ISSN 1342-6893    Online edition: ISSN 2424-1970
Notes on Review This article is a technical report without peer review, and its polished version will be published elsewhere.
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Conference Information
Committee ME  
Conference Date 2017-02-18 - 2017-02-18 
Place (in Japanese) (See Japanese page) 
Place (in English) Kanto Gakuin Univ. 
Topics (in Japanese) (See Japanese page) 
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Paper Information
Registration To ME 
Conference Code 2017-02-ME 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Accurate Feature Point Tracking using Local Invariant Features for Omnidirectional Image Sequence 
Sub Title (in English)  
Keyword(1) Feature Point Tracking  
Keyword(2) Omnidirectional Image  
Keyword(3) Affine Invariant Local Features  
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1st Author's Name Hiroshi Tada  
1st Author's Affiliation Yokohama National University (YNU)
2nd Author's Name Koichi Ichige  
2nd Author's Affiliation Yokohama National University (YNU)
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Speaker Author-1 
Date Time 2017-02-18 11:15:00 
Presentation Time 15 minutes 
Registration for ME 
Paper # ME2017-4 
Volume (vol) vol.41 
Number (no) no.4 
Page pp.13-16 
#Pages
Date of Issue 2017-02-11 (ME) 


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