Introduction to algorithms
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
A Unified Approach to Moving Object Detection in 2D and 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatiotemporal Segmentation Based on Region Merging
IEEE Transactions on Pattern Analysis and Machine Intelligence
W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICPP '97 Proceedings of the international Conference on Parallel Processing
ICPADS '02 Proceedings of the 9th International Conference on Parallel and Distributed Systems
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Fast Branch & Bound Algorithms for Optimal Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two-View Multibody Structure-and-Motion with Outliers through Model Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Branch and Bound Clustering Algorithm
IEEE Transactions on Computers
Perspective n-view multibody structure-and-motion through model selection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Incorporating non-motion cues into 3d motion segmentation
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Branch and bound algorithms for rate-distortion optimized media streaming
IEEE Transactions on Multimedia
Surfaces with occlusions from layered stereo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detection of moving objects in video using a robust motion similarity measure
IEEE Transactions on Image Processing
Globally optimal consensus set maximization through rotation search
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Multi-object reconstruction from dynamic scenes: An object-centered approach
Computer Vision and Image Understanding
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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 guaranties 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.