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Proceedings Paper

3D object recognition based on hierarchical eigen shapes and Bayesian inference
Author(s): Timo Kostiainen; Ilkka Kalliomaeki; Toni Tamminen; Jouko Lampinen
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Paper Abstract

We present results of using Bayesian inference for recovering the 3D shape and texture of an object based on information extracted from a single 2D image. We are using a number of different models for specific object classes. The goal is to combine the classes to a hierarchical structure. Instead of searching for the most probable explanation we estimate the entire posterior distribution of the model parameters using Markov chain Monte Carlo methods. The evaluation of model fit is based on combining edge information with intensity difference between the model and the target image.

Paper Details

Date Published: 5 October 2001
PDF: 9 pages
Proc. SPIE 4572, Intelligent Robots and Computer Vision XX: Algorithms, Techniques, and Active Vision, (5 October 2001); doi: 10.1117/12.444179
Show Author Affiliations
Timo Kostiainen, Helsinki Univ. of Technology (Finland)
Ilkka Kalliomaeki, Helsinki Univ. of Technology (Finland)
Toni Tamminen, Helsinki Univ. of Technology (Finland)
Jouko Lampinen, Helsinki Univ. of Technology (Finland)


Published in SPIE Proceedings Vol. 4572:
Intelligent Robots and Computer Vision XX: Algorithms, Techniques, and Active Vision
David P. Casasent; Ernest L. Hall, Editor(s)

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