Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to Parse Pictures of People
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Pictorial Structures for Object Recognition
International Journal of Computer Vision
Strike a Pose: Tracking People by Finding Stylized Poses
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Using the Inner-Distance for Classification of Articulated Shapes
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Learning to Estimate Human Pose with Data Driven Belief Propagation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Spectral Segmentation with Multiscale Graph Decomposition
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 Using Pairwise Constraints between Parts
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Guiding Model Search Using Segmentation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Shape Guided Object Segmentation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Body Localization in Still Images Using Hierarchical Models and Hybrid Search
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Measure Locally, Reason Globally: Occlusion-sensitive Articulated Pose Estimation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Recovering human body configurations: combining segmentation and recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Proposal maps driven MCMC for estimating human body pose in static images
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Contour Grouping with Partial Shape Similarity
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Hierarchical Vibrations: A Structural Decomposition Approach for Image Analysis
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
International Journal of Computer Vision
Volumetric Features for Video Event Detection
International Journal of Computer Vision
International Journal of Computer Vision
Arbitrary body segmentation in static images
Pattern Recognition
Video search and indexing with reinforcement agent for interactive multimedia services
ACM Transactions on Embedded Computing Systems (TECS) - Special issue on embedded systems for interactive multimedia services (ES-IMS)
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Recognizing humans, estimating their pose and segmenting their body parts are key to high-level image understanding. Because humans are highly articulated, the range of deformations they undergo makes this task extremely challenging. Previous methods have focused largely on heuristics or pairwise part models in approaching this problem. We propose a bottom-up growing, similar to parsing, of increasingly more complete partial body masks guided by a set of parse rules. At each level of the growing process, we evaluate the partial body masks directly via shape matching with exemplars (and also image features), without regard to how the hypotheses are formed. The body is evaluated as a whole, not the sum of its parts, unlike previous approaches. Multiple image segmentations are included at each of the levels of the growing/parsing, to augment existing hypotheses or to introduce ones. Our method yields both a pose estimate as well as a segmentation of the human. We demonstrate competitive results on this challenging task with relatively few training examples on a dataset of baseball players with wide pose variation. Our method is comparatively simple and could be easily extended to other objects. We also give a learning framework for parse ranking that allows us to keep fewer parses for similar performance.