Abstract

We examined the performance of linear and nonlinear processors (filters) for image recognition that are lp-norm optimum in terms of tolerance to input noise and discrimination capabilities. These processors were developed by minimizing the lp norm of the filter output due to the input scene and the output due to the noise. We tested the performance of the lp-norm optimum filters by measuring the average peak-to-sidelobe ratio of the output of the filters for different values of p. We also tested the performance of these filters by placing a target in a scene containing additive noise and a realistic background. For the images presented here, the filters detected the target in the presence of additive noise and a realistic background. The tests conducted show that the discrimination capabilities of the lp-norm filters improve as p decreases (p>1). This is shown by sharper peaks at the target location and higher average peak-to-sidelobe ratios for smaller values of p.

© 1999 Optical Society of America

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