Feature Extraction For Under Sampled Objects In Range Imagery

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Abstract
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.

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