Partial Shape Recognition Using Dynamic Programming
IEEE Transactions on Pattern Analysis and Machine Intelligence
Partial Shape Classification Using Contour Matching in Distance Transformation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Based Detection of Corners of Planar Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Theory of Multiscale, Curvature-Based Shape Representation for Planar Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning flexible models from image sequences
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Active shape models—their training and application
Computer Vision and Image Understanding
A Framework for Automatic Landmark Identification Using a New Method of Nonrigid Correspondence
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Critical Point Detection of Digital Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Condensing Image Databases when Retrieval is Based on Non-Metric Distances
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Multimodal Shape Tracking with Point Distribution Models
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Combining View-Based and Model-Based Tracking of Articulated Human Movements
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
An approach to vision-based person detection in robotic applications
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
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This paper addresses the problem of learning shape models from examples. The contributions are twofold. First, a comparative study is performed of various methods for establishing shape correspondence - based on shape decomposition, feature selection and alignment. Various registration methods using polygonal and Fourier features are extended to deal with shapes at multiple scales and the importance of doing so is illustrated. Second, we consider an appearance-based modeling technique which represents a shape distribution in terms of clusters containing similar shapes; each cluster is associated with a separate feature space. This representation is obtained by applying a novel simultaneous shape registration and clustering procedure on a set of training shapes. We illustrate the various techniques on pedestrian and plane shapes.