Outlier detection and motion segmentation

Conference item

We present a new method for solving the problem of motion segmentation, identifying the objects within an image moving independently of the background. We utilise 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.

Authors: Torr, PHS; Murray, DW

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Society of Photo-Optical Instrumentation Engineers
• Host title: Sensor Fusion VI
• Journal: Proceedings of SPIE
• Volume: 2059
• Pages: 432-443
• Publication date: 1993-08-20
• Event title: Optical Tools for Manufacturing and Advanced Automation 1993
• Event location: Boston, MA, USA
• Event start date: 1993-09-07
• Event end date: 1993-09-10
• DOI: 10.1117/12.150246
• EISSN: 1996-756X
• ISSN: 0277-786X
• Language: English
• Keywords: SBTMR