Abstract
Wavelet feature performance for the detection and recognition of targets from noisy images is investigated. Training patterns with different noise contents are first employed to come up with a statistical model for the dissimilarity of the reference target and noisy inputs. This model is then analyzed with Daubechies wavelet filter with extremal phase and vanishing moment. Simulation results show the potential of wavelet features that can be used in the decision making subsystem to yield high discrimination between target and nontarget. (Author)