Morphological wavelet transform for distortion-invariant object detection in clutter

We developed an approach combining morphological processing and wavelet transforms to detect multiple objects in an input scene. The input scene contains different types of background clutter regions and multiple objects in different classes, with different object aspect views, different object representations, hot/cold/bimodal/partial object variations, and high/low object contrast variations. Our approach provides high detection rates and low false alarm rates. The most computationally demanding operations required are realizable on an optical correlator.