A Mixture Model for Representing Shape Variation (1997)
| Venue: | Image and Vision Computing |
| Citations: | 74 - 6 self |
BibTeX
@INPROCEEDINGS{Taylor97amixture,
author = {Cootes And Taylor and T. F. Cootes and C. J. Taylor},
title = {A Mixture Model for Representing Shape Variation},
booktitle = {Image and Vision Computing},
year = {1997},
pages = {110--119},
publisher = {BMVA Press}
}
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Abstract
The shape variation displayed by a class of objects can be represented as a probability density function, allowing us to determine plausible and implausible examples of the class. Given a training set of example shapes we can align them into a common co-ordinate frame and use kernel based density estimation techniques to represent this distribution. Such an estimate is complex and expensive, so we generate a simpler approximation using a mixture of gaussians. We show how to calculate the distribution, and how it can be used in image search to locate examples of the modelled object in new images.
