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Proceedings Paper

Best viewpoints for active vision classification and pose estimation
Author(s): Michael A. Sipe; David P. Casasent
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Paper Abstract

We advance new active computer vision algorithms that classify objects and estimate their pose from intensity images. Our algorithms automatically reposition the sensor if the class or pose of an object is ambiguous in a given image and incorporate data from multiple object views in determining the final object classification. A feature space trajectory (FST) in a global eigenfeature space is used to represent 3-D distorted views of an object. Assuming that an observed feature vector consists of Gaussian noise added to a point on the FST, we derive a probability density function (PDF) for the observation conditioned on the class and pose of the object. Bayesian estimation and hypothesis testing theory are then used to derive approximations to the maximum a posteriori probability pose estimate and the minimum probability of error classifier. New confidence measures for the class and pose estimates, derived using Bayes theory, determine when additional observations are required as well as where the sensor should be positioned to provide the most useful information.

Paper Details

Date Published: 26 September 1997
PDF: 12 pages
Proc. SPIE 3208, Intelligent Robots and Computer Vision XVI: Algorithms, Techniques, Active Vision, and Materials Handling, (26 September 1997); doi: 10.1117/12.290309
Show Author Affiliations
Michael A. Sipe, Carnegie Mellon Univ. (United States)
David P. Casasent, Carnegie Mellon Univ. (United States)


Published in SPIE Proceedings Vol. 3208:
Intelligent Robots and Computer Vision XVI: Algorithms, Techniques, Active Vision, and Materials Handling
David P. Casasent, Editor(s)

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