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Automated training of 3D morphology algorithm for object recognition

Grayscale morphology has demonstrated a great deal of success in automatic target recognition (ATR) applications with a variety of imagery sources including SAR, IR, visible, and multispectral. However, training the morphology algorithm requires significant experience and is labor intensive. This paper presents an innovative approach for using genetic algorithms (GA) and the classification and regression trees (CART) algorithm to automate morphology algorithm training and optimize detection performance. The GA is used to find the morphology operators by encoding them into binary vectors. The CART algorithm determines the optimum region filtering parameters in conjunction with the morphology operations. Robustness is achieved by regression pruning of the CART generated classification trees. The basic concepts in applying the GA to the design of grayscale morphology filters is described. Our results suggest that the detection performance of a GA designed morphology filter is comparable to that designed by human experts. The automated design method significantly shortens the design process.