Detection involves locating all candidate regions of interest (objects) in a scene independent of the object class with object distortions and contrast differences, etc., present. It is one of the most formidable problems in automatic target recognition, since it involves analysis of every local scene region. We consider new detection algorithms and the fusion of their outputs to reduce the probability of false alarm P(FA) while maintaining high probability of detection P(D). Emphasis is given to detecting obscured targets in infrared imagery.

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We consider the problem of detecting multiple distorted objects in an input scene with clutter. The input scenes contain different types of background clutter and multiple objects in different classes, with different object aspect views, different object representations, hot/cold/bimodal/partial object variations, and high/low contrast object variations. Several new optical morphological operations for use in the above detection problem and in other general low-level image-processing applications are described, and several examples of their use are provided. For difficult detection problems in which high detection rates and low false-alarm rates are required we combine morphological operations and optical wavelet transforms to reduce clutter and improve object detection. The details of this set of filters and initial testresults are given. The most computationally demanding operations required in all cases are realizable on an optical correlator.

We consider morphological processing for clutter reduction and object detection. For detection, we compare a 1)inary and gray-scale Hit-Miss Transform and find that the binary operator is preferable. For clutter reduction, we find gray-scale morphology to be preferable. We present a new gray-scale clutter reduction morphological algorithm for low clutter cases and a new algorithm for high clutter cases. In all morphological processing, we find binary structuring elements to be adequate; this is very attractive for our gray-scale morphology decomposition algorithm and its optical implementation. 0-8194-1