NDLI: Robust detection of degenerate configurations for the fundamental matrix

Content Provider	IEEE Xplore Digital Library
Author	Torr, P.H.S. Zisserman, A. Maybank, S.J.
Copyright Year	1995
Description	Author affiliation: Dept. of Eng. Sci., Oxford Univ., UK (Torr, P.H.S.; Zisserman, A.; Maybank, S.J.)
Abstract	New methods are reported for the detection of multiple solutions (degeneracy) when estimating the fundamental matrix, with specific emphasis on robustness in the presence of data contamination (outliers). The fundamental matrix can be used as a first step in the recovery of structure from motion. If the set of correspondences is degenerate then this structure cannot be accurately recovered and many solutions will explain the data equally well. It is essential that we are alerted to such eventualities. However, current feature matchers are very prone to mismatching, giving a high rate of contamination within the data. Such contamination can make a degenerate data set appear non degenerate, thus the need for robust methods becomes apparent. The paper presents such methods with a particular emphasis on providing a method that will work on real imagery and with an automated (non perfect) feature detector and matcher. It is demonstrated that proper modelling of degeneracy in the presence of outliers enables the detection of outliers which would otherwise be missed. Results using real image sequences are presented. All processing, point matching, degeneracy detection and outlier detection is automatic.<>
Starting Page	1037
Ending Page	1042
File Size	562726
Page Count	6
File Format	PDF
ISBN	0818670428
DOI	10.1109/ICCV.1995.466820
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1995-06-20
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robustness Contamination Solid modeling Pollution measurement Cameras Robots Data engineering Computer vision Detectors Image sequences
Content Type	Text
Resource Type	Article

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