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Proceedings Paper

Probabilistic view clustering in object recognition
Author(s): Octavia I. Camps; Douglas W. Christoffel; Anjali Pathak
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Paper Abstract

To recognize objects and to determine their poses in a scene we need to find correspondences between the features extracted from the image and those of the object models. Models are commonly represented by describing a few characteristic views of the object representing groups of views with similar properties. Most feature-based matching schemes assume that all the features that are potentially visible in a view will appear with equal probability, and the resulting matching algorithms have to allow for 'errors' without really understanding what they mean. PREMIO is an object recognition system that uses CAD models of 3D objects and knowledge of surface reflectance properties, light sources, sensor characteristics, and feature detector algorithms to estimate the probability of the features being detectable and correctly matched. The purpose of this paper is to describe the predictions generated by PREMIO, how they are combined into a single probabilistic model, and illustrative examples showing its use in object recognition.

Paper Details

Date Published: 1 November 1992
PDF: 12 pages
Proc. SPIE 1828, Sensor Fusion V, (1 November 1992); doi: 10.1117/12.131657
Show Author Affiliations
Octavia I. Camps, The Pennsylvania State Univ. (United States)
Douglas W. Christoffel, The Pennsylvania State Univ. (United States)
Anjali Pathak, The Pennsylvania State Univ. (United States)

Published in SPIE Proceedings Vol. 1828:
Sensor Fusion V
Paul S. Schenker, Editor(s)

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