Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
An Integrated Bayesian Approach to Layer Extraction from Image Sequences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Detection and Tracking of Motion Boundaries
International Journal of Computer Vision - Special issue on Genomic Signal Processing
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Learning the Statistics of People in Images and Video
International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Using Temporal Coherence to Build Models of Animals
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Variational Space-Time Motion Segmentation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Learning Layered Motion Segmentation of Video
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Learning to Look at Humans -- What Are the Parts of a Moving Body?
AMDO '08 Proceedings of the 5th international conference on Articulated Motion and Deformable Objects
Seeing the Objects Behind the Dots: Recognition in Videos from a Moving Camera
International Journal of Computer Vision
Proceedings of the 2008 conference on Artificial Intelligence Research and Development: Proceedings of the 11th International Conference of the Catalan Association for Artificial Intelligence
Dense and Deformable Motion Segmentation for Wide Baseline Images
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
Enhanced Local Subspace Affinity for feature-based motion segmentation
Pattern Recognition
Learning generic human body models
AMDO'10 Proceedings of the 6th international conference on Articulated motion and deformable objects
Object segmentation by long term analysis of point trajectories
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
TriangleFlow: optical flow with triangulation-based higher-order likelihoods
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Learning spatio-temporal dependency of local patches for complex motion segmentation
Computer Vision and Image Understanding
Detachable object detection with efficient model selection
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
Robust stroke-based video animation via layered motion and correspondence
Proceedings of the 20th ACM international conference on Multimedia
Online moving camera background subtraction
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Video segmentation with superpixels
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
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We present an unsupervised approach for learning a layered representation of a scene from a video for motion segmentation. Our method is applicable to any video containing piecewise parametric motion. The learnt model is a composition of layers, which consist of one or more segments. The shape of each segment is represented using a binary matte and its appearance is given by the rgb value for each point belonging to the matte. Included in the model are the effects of image projection, lighting, and motion blur. Furthermore, spatial continuity is explicitly modeled resulting in contiguous segments. Unlike previous approaches, our method does not use reference frame(s) for initialization. The two main contributions of our method are: (i) A novel algorithm for obtaining the initial estimate of the model by dividing the scene into rigidly moving components using efficient loopy belief propagation; and (ii) Refining the initial estimate using 驴 β-swap and 驴-expansion algorithms, which guarantee a strong local minima. Results are presented on several classes of objects with different types of camera motion, e.g. videos of a human walking shot with static or translating cameras. We compare our method with the state of the art and demonstrate significant improvements.