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摘要
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. 1. Introduction Morphological image processing is a nonlinear branch in image processing developed by Matheron and Serra in the 1960s, based on geometry and on the mathematical theory of order [1每6]. Morphological image processing has proved to be a powerful tool for binary and grayscale image computer vision processing tasks, such as edge detection, noise suppression, skeletonization, segmentation, pattern recognition, and enhancement [7]. Initial applications of morphological processing were biomedical and geological image analysis problems [8]. In the 1980s, extensions of classical mathematical morphology and connections to other fields were developed by several research groups worldwide along various directions, including computer vision problems, multiscale image processing, statistical analysis, and optimal design of morphological filters, to name just a few. The basic operations in mathematical morphology are the dilation and the erosion, and these operations can be described by logical and arithmetic operators. Dilation and erosion morphological operators can be represented, respectively, by the sum and subtraction of Minkowski sets [9]: In (1), is the original binary image, is the structuring element of the morphological operation, and is the displacement by . Therefore, the dilation operation is obtained by the union of all displacements in relation to the valid elements. In (2), is the 180～ rotation of in relation to its origin. Therefore, the erosion operation corresponds to
- 出版日期2011
