Image Fusion Using Autoassociative-Heteroassociative Neural Networks
AIR FORCE INST OF TECH WRIGHT-PATTERSONAFB OH SCHOOL OF ENGINEERING
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Images are easily recognized, classified, and segmented in short, analyzed by humans. The human brainnervous system the biological computer, performs rapid and accurate image processing. In the current research the concepts of the biological neural system provide the impetus for developing a computational means of fusing image data. Accomplishing this automatic image processing requires features be extracted from each image data set, and the information content fused. Biologically inspired computational models are examined for extracting features by transformations such as Fourier, Gabor, and wavelets, and for processing and fusing the information from multiple images through evaluation of autoassociative neural networks AANNs. Features are obtainable through the use of human visual system models, however, AANNs are limited in accomplishing the desired data fusion. In depth analysis of these networks demonstrated their functionality as data filters requiring careful feature selection to provide the desired image processing. Some features, when using perceptual space concepts, required AANNs to have the ability to process complex valued inputs instead of only real valued data. Human visual system concepts provided an alternative error function metric for training the networks based on the human standard of similarity instead of absolute numerical value. The most significant limitation of AANNs, for this data fusion application, was their inability to process multiple image data sources. The AANN concepts were extended to a new architecture the Autoassociative Heteroassociative Neural Network A-HNN developed for predicting one sensor image from another by using input data values as desired targets.