Morphological and wavelet transforms for object detection and image processing

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.