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Title:Linear and Nonlinear Generative Probabilistic Class Models for Shape Contours
Authors: Graham McNeill ; Sethu Vijayakumar
Date:Jun 2007
Publication Title:International Conference on Machine Learning (ICML '07)
Publication Type:Conference Paper Publication Status:Published
We introduce a robust probabilistic approach to modeling shape contours based on a low-dimensional, nonlinear latent variable model. In contrast to existing techniques that use objective functions in data space without explicit noise models, we are able to extract complex shape variation from noisy data. Most approaches to learning shape models slide observed data points around fixed con- tours and hence, require a correctly labeled reference shape to prevent degenerate solutions. In our method, unobserved curves are reparameterized to explain the fixed data points, so this problem does not arise. The proposed algorithms are suitable for use with arbitrary basis functions and are applicable to both open and closed shapes; their effectiveness is demonstrated through illustrative examples, quantitative assessment on benchmark data sets and a visualization task.
2007 by The University of Edinburgh. All Rights Reserved
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Bibtex format
author = { Graham McNeill and Sethu Vijayakumar },
title = {Linear and Nonlinear Generative Probabilistic Class Models for Shape Contours},
book title = {International Conference on Machine Learning (ICML '07)},
year = 2007,
month = {Jun},

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