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Proceedings Paper

Neural Network Model For Fusion Of Visible And Infrared Sensor Outputs
Author(s): Pongsak Ajjimarangsee; Terrance L. Huntsberger
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Paper Abstract

Integration of outputs from multiple sensors has been the subject of much of the recent research in the machine vision field. This process is useful in a variety of applications, such as three dimensional interpretation of scenes imaged by multiple cameras, integration of visible and range data, and the fusion of multiple types of sensors. The use of multiple types of sensors for machine vision poses the problem of how to integrate the information from these sensors. This paper presents a neural network model for the fusion of visible and thermal infrared sensor outputs. Since there is no human biological system that can be used as a model for integration of these sensor outputs, alternate biological systems for sensory fusions can serve as starting points. In this paper, a model is developed based upon six types of bimodal neurons found in the optic tectum of the rattlesnake. These neurons integrate visible and thermal infrared sensory inputs. The neural network model has a series of layers which include a layer for unsupervised clustering in the form of self-organizing feature maps, followed by a layer which has multiple filters that are generated by training a neural net with experimental rattlesnake response data. The final layer performs another unsupervised clustering for integration of the output from the filter layer. The results of a number of experiments are also presented.

Paper Details

Date Published: 5 January 1989
PDF: 8 pages
Proc. SPIE 1003, Sensor Fusion: Spatial Reasoning and Scene Interpretation, (5 January 1989); doi: 10.1117/12.948926
Show Author Affiliations
Pongsak Ajjimarangsee, University of South Carolina (United States)
Terrance L. Huntsberger, University of South Carolina (United States)

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

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