The detection ofland mines has two fundamental goals: The first is a high detection rate (low probability of missing a mine) and the second is a low false alarm rate. Detection of mines and mine-like objects is generally not difficult; the problem is the high false-alarm rate caused by detection of innocuous objects such as shrapnel or metal junk, or even rocks or voids in the soil. The problem is one of discrimination, not one of detection. In order to maximize the success of achieving this goal, a mine detector needs to incorporate many complementary sensor technologies and to utilize the concept ofsensor data fusion. Two subsystems employ new signal processing techniques which extract certain features from the data that are unique identifiers ofthe mines. These features are the natural magnetic and electromagnetic resonances, which form the impulse response function, or equivalently, the natural frequencies represented by poles in the complex frequency plane. For different objects these are sufficiently distinct that pattern recognition processes can be used to arrive at a probability ofa match to a particular mine.
A machine vision system needing to remain vigilant within its environment must be able to quickly perceive both clearly identifiable objects as well as those that are deceptive or camouflaged (attempting to blend into the background). Humans accomplish this task early in the visual pathways, using five spatially defined forms of processing. These forms are Luminance-defined, Color-defined, Texture-defined, Motion-defined, and Disparity-defined. This paper discusses a visual sensor approach that combines a biological system's strategy to break down camouflage with simple image processing algorithms that may be implemented for real time video. Thermal imaging is added to increase sensing capability. Preliminary filters using MATLAB and operating on digital still images show somewhat encouraging results. Current efforts include implementing the sensor for real-time video processing.
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