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Outlier detection in video sequences under affine projection

Huynh, D.Q. and Heyden, A. (2001) Outlier detection in video sequences under affine projection. In: 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 8 - 14 December, Kauai, Hawaii I-695-I-701.

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    Link to Published Version: http://dx.doi.org/10.1109/CVPR.2001.990543
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    Abstract

    A novel robust method for outlier detection in structure and motion recovery for affine cameras is presented. It is an extension of the well-known Tomasi-Kanade factorization technique (C. Tomasi T. Kanade, 1992) designed to handle outliers. It can also be seen as an importation of the LMedS technique or RANSAC into the factorization framework. Based on the computation of distances between subspaces, it relates closely with the subspace-based factorization methods for the perspective case presented by G. Sparr (1996) and others and the subspace-based-factorization for affine cameras with missing data by D. Jacobs (1997). Key features of the method presented are its ability to compare different subspaces and the complete automation of the detection and elimination of outliers. Its performance and effectiveness are demonstrated by experiments involving simulated and real video sequences.

    Publication Type: Conference Paper
    Murdoch Affiliation: School of Information Technology
    Publisher: IEEE
    Copyright: © 2001 IEEE
    Notes: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This paper appears in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Volume 1, 2001, Pages I695-I701
    URI: http://researchrepository.murdoch.edu.au/id/eprint/6028
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