Automated training of 3D morphology algorithm for object recognition

Michael E. Bullock; David L. Wang; Scott R. Fairchild; Tim J. Patterson

doi:10.1117/12.181022

29 July 1994 Automated training of 3D morphology algorithm for object recognition

Michael E. Bullock, David L. Wang, Scott R. Fairchild, Tim J. Patterson

Author Affiliations +

Proceedings Volume 2234, Automatic Object Recognition IV; (1994) https://doi.org/10.1117/12.181022
Event: SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, 1994, Orlando, FL, United States

Abstract

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.

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Michael E. Bullock, David L. Wang, Scott R. Fairchild, and Tim J. Patterson "Automated training of 3D morphology algorithm for object recognition", Proc. SPIE 2234, Automatic Object Recognition IV, (29 July 1994); https://doi.org/10.1117/12.181022

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