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