Probabilistic Models of Appearance for 3-D Object Recognition

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Probabilistic Models of Appearance for 3-D Object Recognition
We describe how to model the appearance of a 3-D object using multiple views, learn such a model from training images, and use the model for object recognition. The model uses probability distributions to describe the range of possible variation in the object's appearance. These distributions are organized on two levels. Large variations are handled by partitioning training images into clusters corresponding to distinctly different views of the object. Within each cluster, smaller variations are represented by distributions characterizing uncertainty in the presence, position, and measurements of various discrete of appearance. Many types of features are used, ranging in abstraction from edge segments to perceptual groupings and regions. A matching procedure uses the feature uncertainty information to guide the search for a match between model and image. Hypothesized feature pairings are used to estimate a viewpoint transformation taking account of feature uncertainty. These meth...
Arthur R. Pope, David G. Lowe
Added 18 Dec 2010
Updated 18 Dec 2010
Type Journal
Year 2000
Where IJCV
Authors Arthur R. Pope, David G. Lowe
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