A bio-inspired software for segmenting digital images

Abstract

Abstract-Segmentation in computer vision refers to the pro cess of partitioning a digital image into multiple segments (sets of pixels). It has several features which make it suitable for techniques inspired by nature. It can be parallelized , locally solved and the input data can be easily encoded by bio inspired representations. In this paper, we present a new software for performing a segmentation of 2D digital images based on Membrane Computing techniques. I. I NTRODUCTIONNature is a big inspiration source for designing solutions to a broad panoply of problems. Natural Computing studies com putational paradigms inspired from various well known natural phenomena in physics, chemistry and biology! . It abstracts the way in which nature acts, conceiving new computing models. All these computational paradigms have in common the use of an alternative way of encoding the information, adapted to the bio-inspired substrate and the use of intrinsic parallelism of natural processes.In this paper we present a bio-inspired software for solving the Segmentation Problem in Digital Imagery. Segmentation in computer vision (see [11]), refers to the process of partitioning 978-1-4244-6439-5/10/$26.00 ©2010 IEEE Spain magutier@us.es a digital image into multiple segments (sets of pixels). The goal of segmentation is to simplify and/or change the repre sentation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain visual characteristics.Segmentation in Digital Imagery has several features which make it suitable for techniques inspired by nature. One of them is that it can be parallelized and locally solved. Regardless how large is the picture, the segmentation process can be performed in parallel in different local areas of it. Another interesting feature is that the basic necessary information can be easily encoded by bio-inspired representations.In the literature, one can find several attempts for bridging problems from Digital Imagery with Natural Computing as the works by K.G. Subramanian et al.[12], [13] or the work by Chao and Nakayama where Natural Computing and Algebraic Topology are linked by using Neural Networks [14] (extended Kohonen mapping). In this paper, we will use an information encoding and techniques borrowed from Membrane Computing.Membrane Computing is a theoretical model of compu tation inspired by the structure and functioning of cells as living organisms able to process and generate information. The computational devices are called P systems [9]. Roughly speaking, a P system consists of a membrane structure, in the compartments of which one places multi sets of objects which evolve according to given rules. In the most extended model, the rules are applied in a synchronous non-deterministic max imally parallel manner, but some other s...

A Parallel Implementation of the Thresholding Problem by Using Tissue-Like P Systems

“…Subramanian et al [1] or the work by Chao and Nakayama where Natural Computing and Algebraic Topology are linked by using Neural Networks [2] (extended Kohonen mapping). Recently, new approaches have been presented in the framework of Membrane Computing [3,4,5].…”

Membrane computing and image processing: a short survey

“…In recent years, P systems have been used to deal with threshold-based segmentation problems. Díaz-Pernil et al [45] developed an image segmentation method on 2D images using P systems, which was applied to medical image segmentation. Christinal et al [28] presented an image segmentation method based on tissue-like P systems by using the 4-neighborhood relation of pixels in the 2D image.…”

“…Their system uses membrane computing as well as a hardware programming (VHDL) language to propose an ad hoc processor. In another work, Díaz-Pernil et al [45] have proposed a new software tool for segmenting 2D digital images on the basis of tissue-like P system, wherein the object oriented C++ programming language has been used in the implementation part. However, they did not provide a clear explanation regarding the technical aspects of developing the proposed tool.…”

Segmenting images with gradient-based edge detection using Membrane Computing

“…The main motivation for these studies is to bring together Membrane Computing and Picture Grammars. Recently, a new research line has been opened by applying well-known Membrane Computing techniques for solving problems from Digital Imagery as segmentation (Christinal et al, 2009;Christinal et al, 2011;Díaz-Pernil et al, 2010b;Díaz-Pernil et al, 2011), thresholding (Christinal et al, 2010a), smoothing (Peña-Cantillana et al, 2011b or the symmetric dynamic programming stereo algorithm (Gimel'farb et al, 2011).…”