Image Analysis Using Mathematical Morphology
IEEE Transactions on Pattern Analysis and Machine Intelligence
Morphological methods in image and signal processing
Morphological methods in image and signal processing
Genetic programming (videotape): the movie
Genetic programming (videotape): the movie
Total ordering based on space filling curves for multivalued morphology
ISMM '98 Proceedings of the fourth international symposium on Mathematical morphology and its applications to image and signal processing
Automatic programming of morphological machines by PAC learning
Fundamenta Informaticae - Special issue on mathematical morphology
Real-Coded Genetic Algorithms Based on Mathematical Morphology
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
Colour mathematical morphology for neural image analysis
Real-Time Imaging - Special issue: Imaging in bioinformatics part II
Lattice Image Processing: A Unification of Morphological and Fuzzy Algebraic Systems
Journal of Mathematical Imaging and Vision
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
A genetic programming based system for the automatic construction of image filters
Integrated Computer-Aided Engineering
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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.