Outlier detection and motion segmentation

Philip H. S. Torr; David W. Murray

doi:10.1117/12.150246

20 August 1993 Outlier detection and motion segmentation

Philip H. S. Torr, David W. Murray

Proceedings Volume 2059, Sensor Fusion VI; (1993) https://doi.org/10.1117/12.150246
Event: Optical Tools for Manufacturing and Advanced Automation, 1993, Boston, MA, United States

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.

Citation Download Citation

Philip H. S. Torr and David W. Murray "Outlier detection and motion segmentation", Proc. SPIE 2059, Sensor Fusion VI, (20 August 1993); https://doi.org/10.1117/12.150246

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