Linear and nonlinear generative probabilistic class models for shape contours

Graham McNeill, S. Vijayakumar

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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 contours 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.
Original languageEnglish
Title of host publicationProceedings of the 24th international conference on Machine learning
Number of pages8
ISBN (Print)978-1-59593-793-3
Publication statusPublished - 2007


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