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Proceedings Paper

Outlier detection and motion segmentation
Author(s): Philip H. S. Torr; David W. Murray
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Paper Abstract

We present a new method for solving the problem of motion segmentation, identifying the objects within an image moving independently of the background. We utilize the fact that two views of a static 3D point set are linked by a 3 X 3 Fundamental Matrix (F). The Fundamental Matrix contains all the information on structure and motion from a given set of point correspondences and is derived by a least squares method under the assumption that the majority of the image is undergoing a rigid motion. Least squares is the most commonly used method of parameter estimation in computer vision algorithms. However the estimated parameters from a least squares fit can be corrupted beyond recognition in the presence of gross errors or outliers which plague any data from real imagery. Features with a motion independent of the background are those statistically inconsistent from the calculated value of (F). Well founded methods for detecting these outlying points are described.

Paper Details

Date Published: 20 August 1993
PDF: 12 pages
Proc. SPIE 2059, Sensor Fusion VI, (20 August 1993); doi: 10.1117/12.150246
Show Author Affiliations
Philip H. S. Torr, Univ. of Oxford (United Kingdom)
David W. Murray, Univ. of Oxford (United Kingdom)

Published in SPIE Proceedings Vol. 2059:
Sensor Fusion VI
Paul S. Schenker, Editor(s)

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