A Genetic Programming Approach to Reconfigure a Morphological Image Processing Architecture. (21st September 2010)

• Record Type: Journal Article
• Authors: Pedrino, Emerson Carlos; Saito, José Hiroki; Roda, Valentin Obac
• Other Names: Todorovich Elías Academic Editor.
• Is Part Of: International journal of reconfigurable computing. Volume 2011(2011)
• Publication Date: 2010-09-21
• Subjects: Adaptive computing systems -- Periodicals; Adaptive computing systems; Periodicals; 004
• DOI: 10.1155/2011/712494
• Languages: English
• ISSNs: 1687-7195

Abstract

Abstract : 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.