The effect of obscuration's illumination and noise on retrieval and recognition for the generalized minimum mean-square-error filter

We introduce for the first time a novel and unique algorithm for a generalized form for a minimum mean-square-error image processing filter. This algorithm can be used to recognize or retrieve an image that is not only partially obscured by a constant disjoint background, but is also simultaneously blurred and overlaid with additive gaussian noise. Although this algorithm can be applied to many general filter forms that have never been considered before, we test the performance of this filter in four novel obscured-version operating modes: three recognition modes and one retrieval mode. These tests included varying the levels of the background illumination and of the additive white noise, as well as varying the amount of obscuration on the image. Our simulation results show that it is possible to recognize or retrieve images that are as much as 90% obscured, as well as bluffed and noisy with a signal-to-noise ratio of 0.1. We also show that the background illumination of the obscuring object improves the performance of the filter in both its recognition or retrieval modes. This work should be a significant advance in the pattern recognition area for both automatic target recognition and machine vision.