A Layered Motion Representation with Occlusion and Compact Spatial Support
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
The Earth Mover's Distance under Transformation Sets
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Unsupervised Learning of Human Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Metric for Distributions with Applications to Image Databases
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Using Temporal Coherence to Build Models of Animals
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Detecting Pedestrians Using Patterns of Motion and Appearance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A Bayesian Approach to Unsupervised One-Shot Learning of Object Categories
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Real-time scale selection in hybrid multi-scale representations
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
Model-Based Three-Dimensional Interpretations of Two-Dimensional Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Retrieving articulated 3-D models using medial surfaces
Machine Vision and Applications
Unsupervised modeling of objects and their hierarchical contextual interactions
Journal on Image and Video Processing - Special issue on patches in vision
Shape abstraction through multiple optimal solutions
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part II
Multiscale Symmetric Part Detection and Grouping
International Journal of Computer Vision
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We present an algorithm for automatically constructing a decompositional shape model from examples. Unlike current approaches to structural model acquisition, in which one-to-one correspondences among appearance-based features are used to construct an exemplar-based model, we search for many-to-many correspondences among qualitative shape features (multi-scale ridges and blobs) to construct a generic shape model. Since such features are highly ambiguous, their structural context must be exploited in computing correspondences, which are often many-to-many. The result is a Marr-like abstraction hierarchy, in which a shape feature at a coarser scale can be decomposed into a collection of attached shape features at a finer scale. We systematically evaluate all components of our algorithm, and demonstrate it on the task of recovering a decompositional model of a human torso from example images containing different subjects with dissimilar local appearance.