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5 January 1989 Neural Network Model For Fusion Of Visible And Infrared Sensor Outputs
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Proceedings Volume 1003, Sensor Fusion: Spatial Reasoning and Scene Interpretation; (1989)
Event: 1988 Cambridge Symposium on Advances in Intelligent Robotics Systems, 1988, Boston, MA, United States
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.
© (1989) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Pongsak Ajjimarangsee and Terrance L. Huntsberger "Neural Network Model For Fusion Of Visible And Infrared Sensor Outputs", Proc. SPIE 1003, Sensor Fusion: Spatial Reasoning and Scene Interpretation, (5 January 1989);


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