NDLI: Motion segmentation with missing data using PowerFactorization and GPCA

Content Provider	IEEE Xplore Digital Library
Author	Vidal, R. Hartley, R.
Copyright Year	2004
Description	Author affiliation: Center for Imaging Sci., Johns Hopkins Univ., Baltimore, MD, USA (Vidal, R.)
Abstract	We consider the problem of segmenting multiple rigid motions from point correspondences in multiple affine views. We cast this problem as a subspace clustering problem in which the motion of each object lives in a subspace of dimension two, three or four. Unlike previous work, we do not restrict the motion subspaces to be four-dimensional or linearly independent. Instead, our approach deals gracefully with all the spectrum of possible affine motions: from two-dimensional and partially dependent to four-dimensional and fully independent. In addition, our method handles the case of missing data, meaning that point tracks do not have to be visible in all images. Our approach involves projecting the point trajectories of all the points into a 5-dimensional space, using the PowerFactorization method to fill in missing data. Then multiple linear subspaces representing independent motions is fitted to the points in R5 using GPCA. We test our algorithm on various real sequences with degenerate and nondegenerate motions, missing data, perspective effects, transparent motions, etc. Our algorithm achieves a misclassification error of less than 5% for sequences with up to 30% of missing data points.
Sponsorship	IEEE Comput. Soc
File Size	571373
File Format	PDF
ISBN	0769521584
ISSN	10636919
DOI	10.1109/CVPR.2004.1315180
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-06-27
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Motion segmentation Computer vision Cameras Transmission line matrix methods Layout Polynomials Shape measurement Noise shaping Power engineering and energy Systems engineering and theory
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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