Quadratic lters for object classication and detection
نویسنده
چکیده
We present a new class of non-linear correlation lters that produce arbitrary quadratic decision surfaces. These new lters rst linearly combine the outputs from other linear or non-linear lters using complex-valued weights. These linear combinations are then passed through a square-law function and again linearly combined to produce these decision surfaces. The linear correlation lters are designed separately from the non-linear fusion parameters. The output from this new algorithm is thresholded to allow tradeoos between probability of detection and the probability of false alarms to be made. This algorithm is numerically very eecient as it reduces the number of correlation operations required. It is optically and electronically implementable.
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