Multibody structure-and-motion segmentation by branch-and-bound model selection

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
Ninad ThakoorVenkat Devarajan

Abstract

An efficient and robust framework is proposed for two-view multiple structure-and-motion segmentation of unknown number of rigid objects. The segmentation problem has three unknowns, namely the object memberships, the corresponding fundamental matrices, and the number of objects. To handle this otherwise recursive problem, hypotheses for fundamental matrices are generated through local sampling. Once the hypotheses are available, a combinatorial selection problem is formulated to optimize a model selection cost which takes into account the hypotheses likelihoods and the model complexity. An explicit model for outliers is also added for robust segmentation. The model selection cost is minimized through the branch-and-bound technique of combinatorial optimization. The proposed branch-and-bound approach efficiently searches the solution space and guarantees optimality over the current set of hypotheses. The efficiency and the guarantee of optimality of the method is due to its ability to reject solutions without explicitly evaluating them. The proposed approach was validated with synthetic data, and segmentation results are presented for real images.

References

Jan 12, 2005·IEEE Transactions on Pattern Analysis and Machine Intelligence·Michael H Lin, Carlo Tomasi
May 27, 2006·IEEE Transactions on Pattern Analysis and Machine Intelligence·Konrad Schindler, David Suter
Feb 8, 2008·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·H T NguyenA Dev
Jan 1, 1994·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·J A Wang, E H Adelson
Jun 27, 2008·IEEE Transactions on Pattern Analysis and Machine Intelligence·Petr SomolJosef Kittler

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