Feature space trajectory neural net classifier: confidences and thresholds for clutter and low contrast objects

摘要

The feature space trajectory neural net is reviewed. Its advantages over other classifiers are noted; it allows use of smaller training sets, large numbers of hidden layer neurons, low on-line computational loads, higher-order decision surfaces, the ability to reject false class input (clutter) data, etc. New test results on its 3-D distortion-invariant classification performance are provided using a larger object and clutter database, input object contrast differences, a new preprocessing algorithm, and a new feature space. We note the problems with other neural net classifiers that our architecture and algorithm overcomes, the use of different distance thresholds and confidence measures to improve performance, advantages of using adjunct features, and numerous new test results.