Feature extraction from faces using deformable templates
International Journal of Computer Vision
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape quantization and recognition with randomized trees
Neural Computation
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour and Texture Analysis for Image Segmentation
International Journal of Computer Vision
Active Contours: The Application of Techniques from Graphics,Vision,Control Theory and Statistics to Visual Tracking of Shapes in Motion
Machine Learning
Diffusion Snakes: Introducing Statistical Shape Knowledge into the Mumford-Shah Functional
International Journal of Computer Vision
Class-Specific, Top-Down Segmentation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Pedestrian Detection from a Moving Vehicle
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Coupled Geodesic Active Regions for Image Segmentation: A Level Set Approach
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Understanding belief propagation and its generalizations
Exploring artificial intelligence in the new millennium
Markov Random Fields with Efficient Approximations
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Image Parsing: Unifying Segmentation, Detection, and Recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Pictorial Structures for Object Recognition
International Journal of Computer Vision
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Randomized Trees for Real-Time Keypoint Recognition
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
Recovering human body configurations: combining segmentation and recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Constrained Interpolation with Implicit Plane Cubic A-Splines
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Rigid Part Decomposition in a Graph Pyramid
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Mining data with random forests: A survey and results of new tests
Pattern Recognition
Spatio-temporal extraction of articulated models in a graph pyramid
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Hierarchical spatio-temporal extraction of models for moving rigid parts
Pattern Recognition Letters
An introduction to random forests for multi-class object detection
Proceedings of the 15th international conference on Theoretical Foundations of Computer Vision: outdoor and large-scale real-world scene analysis
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We address the problem of segmenting highly articulated video objects in a wide variety of poses. The main idea of our approach is to model the prior information of object appearance via random forests. To automatically extract an object from a video sequence, we first build a random forest based on image patches sampled from the initial template. Owing to the nature of using a randomized technique and simple features, the modeled prior information is considered weak, but on the other hand appropriate for our application. Furthermore, the random forest can be dynamically updated to generate prior probabilities about the configurations of the object in subsequent image frames. The algorithm then combines the prior probabilities with low-level region information to produce a sequence of figure-ground segmentations. Overall, the proposed segmentation technique is useful and flexible in that one can easily integrate different cues and efficiently select discriminating features to model object appearance and handle various articulations.