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Proceedings Paper

Multisensor Object Recognition From 3D Models
Author(s): Tom Miltonberger; Doug Morgan; Greg Orr
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Paper Abstract

Multisensor fusion for object recognition, particularly when multiple sensor platforms and phenomenologies are considered, stresses the state of the art in model-based Image Understanding. The discrimination power of the algorithms depends on accumulation of evidence from diverse sources and on properly applying known model constraints to sensor data. In this paper we describe a model-based multiple-hypothesis Bayesian approach to recognition that has roots in detection and estimation theory. We also describe an approach to object modeling that utilizes an object-based representation that allows multiple geometric representations and multiple, alternative decompositions of the object model. Initial implementations of these ideas have been incorporated into a model-based vision testbed and are currently undergoing testing and evaluation.

Paper Details

Date Published: 5 January 1989
PDF: 11 pages
Proc. SPIE 1003, Sensor Fusion: Spatial Reasoning and Scene Interpretation, (5 January 1989); doi: 10.1117/12.948927
Show Author Affiliations
Tom Miltonberger, Advanced Decision Systems (United States)
Doug Morgan, Advanced Decision Systems (United States)
Greg Orr, Advanced Decision Systems (United States)

Published in SPIE Proceedings Vol. 1003:
Sensor Fusion: Spatial Reasoning and Scene Interpretation
Paul S. Schenker, Editor(s)

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