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This paper describes a f u u y set based approach to the detection of landmines using a novel Ground Penetrating Radar (GPR) imaging system. The GPR produces a threedimensional array of intensity values, representing a volume below the surface of the ground. Multiple prototypes are generated from fum clustering of gradient features on training data, and a fuzzy confidence is then constructed for the test data from the "object" prototypes. This confidence plane is used to automatically detect objects, which are then scored by the ground truth information. Results on the training and testing with the DARPA Backgrounds data set (open jields) and mine lanes (roads) are analyzed.

I n this paper, we present several fuzzy and robust preprocessing algorithms. The algorithms are specifically designed for target detection in ladar range images. W e discuss a fuzzy logic based filtering syst e m that eliminates impulse noise and smoothes images without destroying the details. W e present a robust contrast enhancement filter that highlights the target pixels. W e also present a background subtraction method based o n a robust line-fitting algorithm.