A Genetic Programming Approach to Reconfigure a Morphological Image Processing Architecture

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.

A multiobjective metaheuristic approach for morphological filters on many-core architectures

“…of a MM filter has a strong dependence on the size of the structuring element B. A significant reduction in complexity can be achieved by decomposing the structuring elements into simpler components, at the cost however of loss of filter quality [29].…”

A genetic programming based system for the automatic construction of image filters

“…The main purpose of this case study was also to create a flexible linear procedure that could be used by a reconfigurable processor, to achieve high performance processing, as described in [30]. It is important to mention that some of the proposals (such as those described in [28,32]) only process binary images and do not implement the convolution instruction.…”

Non Linear Algorithm for Automatic Image Processing Applications in FPGAs

“…Initial applications of morphological processing were biomedical and geological image analysis problems. In the 1980´s, extensions of classical mathematical morphology and connections to other fields were developed by several research groups worldwide along various directions, including: computer vision problems, multi scale image processing, statistical analysis, and optimal design of morphological filters, to name just a few (Pedrino et al, 2010). The basic operations in mathematical morphology are the dilation and the erosion, and these operations can be described by logical and arithmetic operators.…”

“…There are several ways of establishing the order, e.g., ordering by one component, canonical ordering, ordering by distance and lexicographical ordering (Chanussot & Lambert, 1998). Once these orders are defined, then the morphological operators are defined in the classical way (Pedrino, 2010).…”