Physiologically motivated image fusion using pulse-coupled neural networks

Randy P. Broussard; Steven K. Rogers

doi:10.1117/12.235981

22 March 1996 Physiologically motivated image fusion using pulse-coupled neural networks

Proceedings Volume 2760, Applications and Science of Artificial Neural Networks II; (1996) https://doi.org/10.1117/12.235981
Event: Aerospace/Defense Sensing and Controls, 1996, Orlando, FL, United States

Abstract

This paper uses a high level vision model to describe the information passing and linking within the primate visual system. Information linking schemes, such as state dependent modulation and temporal synchronization, are presented as methods the vision system uses to combine information using expectation to fill in missing information and remove unneeded information. The possibility of using linking methods derived from physiologically based theoretical models to combine current image processing techniques for pattern recognition purposes is investigated. These image processing techniques are transforms such as (but not limited to) wavelet filters, hit or miss filters, morphological filters, and difference of gausian filters. These particular filters are chosen because they simulate functions that are performed in the primate visual system. To implement the physiologically motivated linking methods, the Pulse Coupled Neural Network (PCNN) is chosen as a basic building block for the vision model which performs linking at the neuronal pulse level. Last, an image fusion network which incorporates information linking based on the PCNN is described, and initial results are presented.

Citation Download Citation

Randy P. Broussard and Steven K. Rogers "Physiologically motivated image fusion using pulse-coupled neural networks", Proc. SPIE 2760, Applications and Science of Artificial Neural Networks II, (22 March 1996); https://doi.org/10.1117/12.235981

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