This paper presents the first physiologically motivated pulse coupled neural network (PCNN)-based image fusion network for object detection. Primate vision processing principles, such as expectation driven filtering, state dependent modulation, temporal synchronization, and multiple processing paths are applied to create a physiologically motivated image fusion network. PCNN's are used to fuse the results of several object detection techniques to improve object detection accuracy. Image processing techniques (wavelets, morphological, etc.) are used to extract target features and PCNN's are used to focus attention by segmenting and fusing the information. The object detection property of the resulting image fusion network is demonstrated on mammograms and Forward Looking Infrared Radar (FLIR) images. The network removed 94% of the false detections without removing any true detections in the FLIR images and removed 46% of the false detections while removing only 7% of the true detections in the mammograms. The model exceeded the accuracy obtained by any individual filtering methods or by logical ANDing the individual object detection technique results.

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This paper will review recent advances in the applications of artificial neural network technology to problems in automatic target recognition. The application of feedforward networks for segmentation, feature extraction, and classification of targets in Forward Looking Infrared (FLIR) and laser radar range scenes will be presented. Biologically inspired Gabor functions will be shown to be a viable alternative to heuristic image processing techniques for segmentation. The use of local transforms, such as the Gabor transform, fed into a feedforward network is proposed as an architecture for neural based segmentation. Techniques for classification of segmented blobs will be reviewed along with neural network procedures for determining relevant features. A brief review of previous work on comparing neural network based classifiers to conventional Bayesian and K-nearest neighbor techniques will be presented. Results from testing several alternative learning algorithms for these neural network classifiers are presented. A technique for fusing information from multiple sensors using neural networks is presented and conclusions are made.