Presentation 2006/1/3
AN INLIER SELECTION METHOD BY USING 4-DIMENSINAL SUBSPACE(International Workshop on Advanced Image Technology 2006)
Takashi Noyori, Hiroyasu Sakamoto,
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Abstract(in English) In this paper, we propose an inlier selection method in feature points tracking of an image sequence. Kanade-Lucas-Tomasi's well-known feature points tracking method [1] (KLT method) may include tracking errors frequently in bad condition. Several research groups introduced outlier removing schemes into the KLT method by using 4-dimensional (4-D) subspace spanned by an orthogonal basis and RANSAC based method sampled 4 feature points out of many points. In our method, a few dozens of feature points are randomly sampled, and the 4-D subspace is spanned by a skew basis which has successfully identified feature point correspondences in our former research [2]. The skew basis provides a measure for deviation from the kernel of the 4-D subspace. Our numerical results show that the skew basis approximates 4-D subspace much better than the orthogonal one. The proposed method is effective as an inlier selection method accompanied by 3-D reconstruction scheme with repeatedly complementing additional feature points.
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Paper # IE2005-193
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Conference Date 2006/1/3(1days)
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Language ENG
Title (in Japanese) (See Japanese page)
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Title (in English) AN INLIER SELECTION METHOD BY USING 4-DIMENSINAL SUBSPACE(International Workshop on Advanced Image Technology 2006)
Sub Title (in English)
Keyword(1)
1st Author's Name Takashi Noyori
1st Author's Affiliation Graduate School of Design, Kyushu Inst. of Design()
2nd Author's Name Hiroyasu Sakamoto
2nd Author's Affiliation Faculty of Design, Kyushu University
Date 2006/1/3
Paper # IE2005-193
Volume (vol) vol.105
Number (no) 501
Page pp.pp.-
#Pages 6
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