Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Deformable template models: a review
Signal Processing - Special issue on deformable models and techniques for image and signal processing
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Methods for Finding People
International Journal of Computer Vision
Strategies for image segmentation combining region and boundary information
Pattern Recognition Letters
Class-Specific, Top-Down Segmentation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Clothed People Detection in Still Images
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pictorial Structures for Object Recognition
International Journal of Computer Vision
Combining Top-Down and Bottom-Up Segmentation
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 4 - Volume 04
Image Parsing: Unifying Segmentation, Detection, and Recognition
International Journal of Computer Vision
Modeling Prior Shape and Appearance Knowledge in Watershed Segmentat
CRV '05 Proceedings of the 2nd Canadian conference on Computer and Robot Vision
Spatial Priors for Part-Based Recognition Using Statistical Models
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Interactive Graph Cut Based Segmentation with Shape Priors
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
MosaicShape: Stochastic Region Grouping with Shape Prior
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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 Using Pairwise Constraints between Parts
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Recovering 3D Human Body Configurations Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local distance preservation in the GP-LVM through back constraints
ICML '06 Proceedings of the 23rd international conference on Machine learning
Training Deformable Models for Localization
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Human Limb Delineation and Joint Position Recovery Using Localized Boundary Models
WMVC '07 Proceedings of the IEEE Workshop on Motion and Video Computing
Recovering human body configurations: combining segmentation and recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Shape context and chamfer matching in cluttered scenes
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Comparison between immersion-based and toboggan-based watershed image segmentation
IEEE Transactions on Image Processing
Morphological grayscale reconstruction in image analysis: applications and efficient algorithms
IEEE Transactions on Image Processing
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We propose a new algorithm for simultaneous localization and figure-ground segmentation where coupled region-edge shape priors are involved with two different but complementary roles. We resort to a segmentation-based hypothesis-and-test paradigm in this research, where the region prior is used to form a segmentation and the edge prior is used to evaluate the validity of the formed segmentation. Our fundamental assumption is that the optimal shape-constrained segmentation that maximizes the agreement with the edge prior occurs at the correctly hypothesized location. Essentially, the proposed algorithm addresses a mid-level vision issue that aims at producing a map image for part detection useful for high-level vision tasks. Our experiments demonstrated that this algorithm offers promising results in terms of both localization and segmentation.