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International Journal of Reconfigurable Computing
Volume 2011, Article ID 712494, 10 pages
Research Article

A Genetic Programming Approach to Reconfigure a Morphological Image Processing Architecture

1Computer Science Department, Federal University of São Carlos, Rodovia Washington Luís, km 235, 13565-905 São Carlos, SP, Brazil
2Department of Electrical Engineering, University of São Paulo, Avenida Trabalhador São Carlense, 400 13566-590 São Carlos SP, Brazil

Received 20 May 2010; Accepted 10 September 2010

Academic Editor: Elías Todorovich

Copyright © 2011 Emerson Carlos Pedrino et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Mathematical morphology supplies powerful tools for low-level image analysis. Many applications in computer vision require dedicated hardware for real-time execution. The design of morphological operators for a given application is not a trivial one. Genetic programming is a branch of evolutionary computing, and it is consolidating as a promising method for applications of digital image processing. The main objective of genetic programming is to discover how computers can learn to solve problems without being programmed for that. In this paper, the development of an original reconfigurable architecture using logical, arithmetic, and morphological instructions generated automatically by a genetic programming approach is presented. The developed architecture is based on FPGAs and has among the possible applications, automatic image filtering, pattern recognition and emulation of unknown filter. Binary, gray, and color image practical applications using the developed architecture are presented and the results are compared with similar techniques found in the literature.