Using Membrane Computing for Effective Homology

Membrane computing is a well-known research area in computer science inspired by the organization and behavior of live cells and tissues. Their computational devices, called P systems, work in parallel and distributed mode and the information is encoded by multisets in a localized manner. All these features make P systems appropriate for dealing with digital images. In this paper, some of the open research lines in the area are presented, focusing on segmentation problems, skeletonization and algebraic-topological aspects of the images. An extensive bibliography about the application of membrane computing to the study of digital images is also provided.

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“…Díaz-Pernil et al use in [41,43] a well-known tool from membrane computing, promoters. They are used to speed up the membrane algorithms.…”

Section: Effective Homologymentioning

confidence: 99%

Membrane computing and image processing: a short survey

2019

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“…One of the most prominent features of MC is its capability of generating exponential growth space over a polynomial time, which makes it a promising method to resolve the conflict between the ever-increasing amount of data in the image processing field and the backward computing power of conventional computer [20]. In recent years, image edge detection and image segmentation [21,22,23,24], image smoothing [25], obtaining homology groups of 2D images [26,27], counting cells [28], Enzymatic numerical P systems image thinning [29] and corner detection [30] in MC framework have been vividly studied. In the previous literature about MC and image processing, much work is based on tissue-like P systems.…”

Section: Introductionmentioning

confidence: 99%

A Resolution-Free Parallel Algorithm for Image Edge Detection within the Framework of Enzymatic Numerical P Systems

et al. 2019

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Image edge detection is a fundamental problem in image processing and computer vision, particularly in the area of feature extraction. However, the time complexity increases squarely with the increase of image resolution in conventional serial computing mode. This results in being unbearably time consuming when dealing with a large amount of image data. In this paper, a novel resolution free parallel implementation algorithm for gradient based edge detection, namely EDENP, is proposed. The key point of our method is the introduction of an enzymatic numerical P system (ENPS) to design the parallel computing algorithm for image processing for the first time. The proposed algorithm is based on a cell-like P system with a nested membrane structure containing four membranes. The start and stop of the system is controlled by the variables in the skin membrane. The calculation of edge detection is performed in the inner three membranes in a parallel way. The performance and efficiency of this algorithm are evaluated on the CUDA platform. The main advantage of EDENP is that the time complexity of O ( 1 ) can be achieved regardless of image resolution theoretically.

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“…Here, to maintain a strong link between this computational framework and algebraic topology tools is a priority. In this way, we obtain a topological description of the digital objects which allows us to understand, efficiently compute and handle advanced topological information of kD digital objects and images [2,8]. For this purpose, we present a new methodology to develop algorithms in topological analysis of digital images.…”

Section: Introductionmentioning

confidence: 99%