Automatic extraction of deformable part models
International Journal of Computer Vision
3-D Shape Recovery Using Distributed Aspect Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
International Journal of Computer Vision
Robust and Efficient Detection of Salient Convex Groups
IEEE Transactions on Pattern Analysis and Machine Intelligence
An efficient search algorithm to find the elementary circuits of a graph
Communications of the ACM
Perceptual Grouping for Contour Extraction
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Globally Optimal Grouping for Symmetric Boundaries
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Groups of Adjacent Contour Segments for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour grouping and abstraction using simple part models
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
On the number of cycles in planar graphs
COCOON'07 Proceedings of the 13th annual international conference on Computing and Combinatorics
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In recent work [1], we introduced a framework for modelbased perceptual grouping and shape abstraction using a vocabulary of simple part shapes. Given a user-defined vocabulary of simple abstract parts, the framework grouped image contours whose abstract shape was consistent with one of the part models. While the results showed promise, the representational gap between the actual image contours that make up an exemplar shape and the contours that make up an abstract part model is significant, and an abstraction of a group of image contours may be consistent with more than one part model; therefore, while recall of ground-truth parts was good, precision was poor. In this paper, we address the precision problem by moving the camera and exploiting spatiotemporal constraints in the grouping process. We introduce a novel probabilistic, graph-theoretic formulation of the problem, in which the spatiotemporal consistency of a perceptual group under camera motion is learned from a set of training sequences. In a set of comprehensive experiments, we demonstrate (not surprisingly) how a spatiotemporal framework for part-based perceptual grouping significantly outperforms a static image version.