Neural Networks
Shape-Based Human Detection and Segmentation via Hierarchical Part-Template Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classification and numbering of teeth in dental bitewing images
Pattern Recognition
Bone graphs: Medial shape parsing and abstraction
Computer Vision and Image Understanding
The pre-image problem in kernel methods
IEEE Transactions on Neural Networks
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This study aims to develop an estimation method for a shape space. In this work, ‘shape space' is a nonlinear subspace formed by a class of visual shapes, in which the continuous change in shapes is naturally represented. By estimating the shape space, various operations dealing with shapes, such as identification, classification, recognition, and interpolation can be carried out in the shape space. A higher-rank of self-organizing map (SOM2) is employed as an implementation of the shape space estimation method. Simulation results show the capabilities of the method.