Best viewpoints for active vision classification and pose estimation

Michael A. Sipe; David P. Casasent

doi:10.1117/12.290309

26 September 1997 Best viewpoints for active vision classification and pose estimation

Michael A. Sipe, David P. Casasent

Proceedings Volume 3208, Intelligent Robots and Computer Vision XVI: Algorithms, Techniques, Active Vision, and Materials Handling; (1997) https://doi.org/10.1117/12.290309
Event: Intelligent Systems and Advanced Manufacturing, 1997, Pittsburgh, PA, United States

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.

Citation Download Citation

Michael A. Sipe and David P. Casasent "Best viewpoints for active vision classification and pose estimation", Proc. SPIE 3208, Intelligent Robots and Computer Vision XVI: Algorithms, Techniques, Active Vision, and Materials Handling, (26 September 1997); https://doi.org/10.1117/12.290309

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