Nonlinear correlation filter and morphology neural networks for image pattern and automatic target recognition

The most important property of a pattern recognition system is it's generalization capability. Previous research shows that neural networks generalize well and approximate arbitrarily complex functions. Feature extraction and decision-making are both important components. Unfortunately, designing effective feature extraction procedures is a very difficult task. For this reason, a heterogeneous neural network that can learn feature extraction and classification simultaneously is very attractive.; The nonlinear correlation filter neural networks (NCFNN) learn correlation filters in the frequency domain and simultaneously learn a nonlinear combination of the outputs of the correlation filters for object detection. Two morphological neural networks, the generalized-mean morphology neural network (GMMNN) and the ordinary morphology neural network (OMNN), learn morphological structuring elements as feature extractors simultaneously with classification. They provide a general problem-independent methodology for designing morphological structuring elements. They perform feature extraction using a novel gray-scale Hit-Miss transform. The OMNN is invariant to shifts in gray-scale.; The GMMNN and the OMNN were applied to pattern recognition problems. For binary handwritten digits, they produced performance comparable to that obtained using the ordinary linear shared-weight neural network (LSNN and NBLSNN) that has been used by others previously. However, the OMNN trained faster. For gray-scale patterns, the morphological neural networks produced superior performance.; The LSNN, the OMNN and the NCFNN were applied to automatic target recognition (ATR) problems. Two data sets were used: Forward Looking Infrared image scenes of tanks and visual image scenes of a parking lot containing occluded vehicles. Several performance measurements and target-aim-point selection algorithms were defined. The OMNN performed significantly better than the other; especially at detecting occluded vehicles and reducing false alarm rates. All the networks performed significantly better than a Minimum-Average-Correlation-Energy filter technique.; The new neural networks provide a general methodology for designing nonlinear filters without specific knowledge of the problem domain. They performed better than existing shared-weight network and matched filter approaches. In particular, the OMNN produced the best performance. It trained relatively quickly and is independent of shifts in gray-level. The NCFNN, the LSNN and the NBLSNN all produced similar performance rates. These networks are not problem-specific and can be widely used for other pattern recognition and ATR problems.