Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Motion Competition: A Variational Approach to Piecewise Parametric Motion Segmentation
International Journal of Computer Vision
Level Set Evolution without Re-Initialization: A New Variational Formulation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Level Set Based Shape Prior Segmentation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Unified Algebraic Approach to 2-D and 3-D Motion Segmentation and Estimation
Journal of Mathematical Imaging and Vision
International Journal of Computer Vision
Modeling, Clustering, and Segmenting Video with Mixtures of Dynamic Textures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding
Human Action Recognition by Semilatent Topic Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Region-level motion-based foreground segmentation under a Bayesian network
IEEE Transactions on Circuits and Systems for Video Technology
Computer Vision and Image Understanding
Geodesic active regions and level set methods for motion estimation and tracking
Computer Vision and Image Understanding
Colour, texture, and motion in level set based segmentation and tracking
Image and Vision Computing
Coherent phrase model for efficient image near-duplicate retrieval
IEEE Transactions on Multimedia
International Journal of Computer Vision
Video segmentation based on motion coherence of particles in a video sequence
IEEE Transactions on Image Processing
A unified tensor level set for image segmentation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
Effective level set image segmentation with a kernel induced data term
IEEE Transactions on Image Processing
Multibody structure-and-motion segmentation by branch-and-bound model selection
IEEE Transactions on Image Processing
Semantics-preserving bag-of-words models and applications
IEEE Transactions on Image Processing
A streakline representation of flow in crowded scenes
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Motion-based unusual event detection in human crowds
Journal of Visual Communication and Image Representation
Real-Time Motion Segmentation of Sparse Feature Points at Any Speed
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Image Processing
Multiple motion segmentation with level sets
IEEE Transactions on Image Processing
A MAP Approach for Joint Motion Estimation, Segmentation, and Super Resolution
IEEE Transactions on Image Processing
IEEE Transactions on Circuits and Systems for Video Technology
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In scenes with collectively moving objects, to disregard the individual objects and take the entire group into consideration for motion characterization is a promising approach with wide application prospects. In contrast to studies on the segmentation of independently moving objects, our purpose is to construct a segmentation of these objects to characterize their motions at a macroscopic level. In general, the collectively moving objects in a group have very similar motion behavior with their neighbors and appear as a kind of global collective motion. This paper presents a joint segmentation approach for these collectively moving objects. In our model, we extract these macroscopic movement patterns based on optical flow field sequences. Specifically, a group of collectively moving objects correspond to a region where the optical flow field has high magnitude and high local direction coherence. As a result, our problem can be addressed by identifying these coherent optical flow field regions. The segmentation is performed through the minimization of a variational energy functional derived from the Bayes classification rule. Specifically, we use a bag-of-words model to generate a codebook as a collection of prototypical optical flow patterns, and the class-conditional probability density functions for different regions are determined based on these patterns. Finally, the minimization of our proposed energy functional results in the gradient descent evolution of segmentation boundaries which are implicitly represented through level sets. The application of our proposed approach is to segment and track multiple groups of collectively moving objects in a large variety of real-world scenes.