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In order to increase the performance of a laser range sensor system one can develop techniques for detecting and classifying objects with undersampled imagery. The goal is to be able to detect and classify a limited set of objects with less than 100 samples on the object. The objects of interest can be modeled as rectangular solids. The sensor system discussed has a low depression angle scan geometry (10 to 20 degrees below horizontal); this means that the sensor sees both the front face and top of the object. The sensor undersamples in the downtrack direction with sample spacing approximately half the width of the objects of interest. The crosstrack sample spacing is close to the Nyquist criteria. This paper assumes that a detection window containing the object of interest is available. Three techniques for extracting the basic geometric features (i.e. length, width and height) are discussed. The first two approaches to extracting the length and width treat the object as a blob and use the detected extrema. These techniques will be compared to another which detects at least two opposing corners and the orientation of the object.

This paper describes the application of a visual pattern recognition neural network in a hybrid model based automatic target recognition (ATR) system. This neural itwxk forms the feature extrtion front end af the ATR and is derived from the Neocognitron network first proposed by K. Fukushima. Fx complex taiget recognition, modiflcatiis to the basic Neocognitron network paradigm were required to enhance robustrss against image distortions due to undersampling (aliasing) and poor feature selection during training. The focus of the paper will be on the enhancements, their rationale, and on the use of the network as a self-organizing feature extractim element of an KFR. Results of experiments with the overall A['R system against target imagery will be shown and discussed. 0-81 94-0874-3/92/$4.O0 Downloaded From: http://proceedings.spiedigitallibrary.org/ on 06/21/2015 Terms of Use: http://spiedl.org/terms SP!E Vol. 1709 Applications of Artificial Neural Networks III (1992)135 Downloaded From: http://proceedings.spiedigitallibrary.org/ on 06/21/2015 Terms of Use: http://spiedl.org/terms

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