| ECVision indexed and annotated bibliography of cognitive computer vision publications This bibliography was created by Hilary Buxton and Benoit Gaillard, University of Sussex, as part of ECVision Specific Action 8-1 The complete text version of this BibTeX file is available here: ECVision_bibliography.bib
P.~H.~S. Torr and A. Zisserman and S. Maybank
ABSTRACT
We present a new method for the detection of multiple solutions or degeneracy when estimating the Fundamental Matrix, with specfic emphasis on robustness to data contamination (mismatches). The Fundamental Matrix encapsulates all the information on camera motion and internal parameters available from image feature correspondences between two views. It is often used as a first step in structure from motion algorithms. If the set of correspondences is degenerate, then this structure cannot be accurately recovered and many solutions explain the data equally well. It is essential that we are alerted to such eventualities. As current feature matchers are very prone to mismatching the degeneracy detection method must also be robust to outliers. In this paper a definition of degeneracy is given and all two view non-degenerate and degenerate cases are catalogued in a logical way by introducing the language of varieties from algebraic geometry. It is then shown how each of the cases can be robustly determined from image correspondences via a scoring function we develop. These ideas define a methodology which allows the simultaneously detection of degeneracy and outliers. The method is called, PLUNDER-DL, and is a generalisation of the robust estimator RANSAC. The method is evaluated on many differing pairs of real images. In particular it is demonstrated that proper modelling of degeneracy in the presence of outliers enables the detection of mismatches which would otherwise be missed. All processing including point matching, degeneracy detection and outlier detection is automatic. Keywords: fundamental matrix, degeneracy, robust methods, motion analysis, epipolar geometry, model selection, MDL. |
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