Representation and Self-Similarity of Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmenting by Seeking the Symmetry Axis
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Skeleton Pruning by Contour Partitioning with Discrete Curve Evolution
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computing Stable Skeletons with Particle Filters
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Contour Grouping Based on Contour-Skeleton Duality
International Journal of Computer Vision
Chain of circles for matching and recognition of planar shapes
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Journal of Visual Communication and Image Representation
A skeleton family generator via physics-based deformable models
IEEE Transactions on Image Processing
A hand gesture recognition system based on local linear embedding
Journal of Visual Languages and Computing
Shape matching using coarse descriptors
International Journal of Computational Vision and Robotics
Matching noisy outline contours using a descriptor reduction approach
ICISP'12 Proceedings of the 5th international conference on Image and Signal Processing
Automatic execution of workflows on laser-scanned data for extracting bridge surveying goals
Advanced Engineering Informatics
Empirical mode decomposition on skeletonization pruning
Image and Vision Computing
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We briefly describe a generic statistical framework for representing the shapes of animate objects using principal component analysis and stochastic shape grammars. Such a representation scheme gives a formalism for solving the inverse problem-object recognition. Then we show: how these representations can be extracted from 2D silhouettes by a novel method for skeleton extraction and shape segmentation; how a similarity metric can be defined on this shape space; and how we can perform recognition in a bottom up/top down loop. The system is demonstrated to be stable in the presence of noise, the absence of parts, the presence of additional parts, and considerable variations in articulation and viewpoint. Successful categorization is demonstrated on a dataset of seventeen categories of animate objects.