Knowledge-based part correspondence
Pattern Recognition
Detection and recognition of contour parts based on shape similarity
Pattern Recognition
Contour Grouping with Partial Shape Similarity
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Shape retrieval with eigen-CSS search
Image and Vision Computing
Classification of silhouettes using contour fragments
Computer Vision and Image Understanding
Shape classification based on skeleton path similarity
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
A similarity-based approach for shape classification using Aslan skeletons
Pattern Recognition Letters
Shape recognition based on Kernel-edit distance
Computer Vision and Image Understanding
Shape matching and classification using height functions
Pattern Recognition Letters
Multi-feature structure fusion of contours for unsupervised shape classification
Pattern Recognition Letters
Shape classification by manifold learning in multiple observation spaces
Information Sciences: an International Journal
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Both example-based and model-based approaches for classifying contour shapes can encounter difficulties when dealing with classes that have large nonlinear variability, especially when the variability is structural or due to articulation. This paper proposes a part-based approach to address this problem. Bayesian classification is performed within a three-level framework which consists of models for contour segments, for classes, and for the entire database of training examples. The class model enables different parts of different exemplars of a class to contribute to the recognition of an input shape. The method is robust to occlusion and is invariant to planar rotation, translation, and scaling. Furthermore, the method is completely automated. It achieves 98% classification accuracy on a large database with many classes.