Skeletonizing Images by Using Spiking Neural P Systems

Skeletonizing an image is representing a shape with a small amount of information by converting the initial image into a more compact representation and keeping the meaning features. In this paper we use spiking neural P systems to solve this problem. Based on such devices, a parallel software has been implemented on the GPU architecture. Some real-world applications and open lines for future research are also presented.Ministerio de Ciencia e Innovación TIN2008-04487-EMinisterio de Ciencia e Innovación TIN-2009-13192Junta de Andalucía P08-TIC-0420

Self-constructing Recognizer P Systems

Usually, the changes produced in the membrane structure of a P system are considered side effects. The output of the computation is encoded as a multiset placed in a specific region and the membrane structure in the halting configuration is not considered important. In this paper we explore P systems where the target of the computation is the construction of a new membrane structure according its set of rules. The new membrane structure can be considered as the initial one of a new self-constructed P system. We focus on the self-construction of recognizer P systems and illustrates the definition with a study of the self-construction P systems working as decision trees for solving Machine Learning decision problems.Ministerio de Economía y Competitividad TIN2012-3743

A parallel algorithm for skeletonizing images by using spiking neural P systems

Skeletonization is a common type of transformation within image analysis. In general, the image B is a skeleton of the black and white image A, if the image B is made of fewer black pixels than the image A, it does preserve its topological properties and, in some sense, keeps its meaning. In this paper, we aim to use spiking neural P systems (a computational model in the framework of membrane computing) to solve the skeletonization problem. Based on such devices, a parallel software has been implemented within the Graphics Processors Units (GPU) architecture. Some of the possible real-world applications and new lines for future research will be also dealt with in this paper.Ministerio de Ciencia e Innovación TIN2008-04487-EMinisterio de Ciencia e Innovación TIN-2009-13192Junta de Andalucía P08-TIC-0420

Parallel Skeletonizing of Digital Images by Using Cellular Automata

Recent developments of computer architectures together with alternative formal descriptions provide new challenges in the study of digital Images. In this paper we present a new implementation of the Guo & Hall algorithm [8] for skeletonizing images based on Cellular Automata. The implementation is performed in a real-time parallel way by using the GPU architecture. We show also some experiments of skeletonizing traffic signals which illustrates its possible use in real life problems.Ministerio de Ciencia e Innovación TIN2008-04487-EMinisterio de Ciencia e Innovación TIN2009–13192Junta de Andalucía P08–TIC-04200Ministerio de Educación y Ciencia MTM2009-12716Universidad del Pais Vasco EHU09/0

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

In this paper we present a parallel algorithm to solve the thresholding problem by using Membrane Computing techniques. This bio-inspired algorithm has been implemented in a novel device architecture called CUDATM, (Compute Unified Device Architecture). We present some examples, compare the obtained time and present some research lines for the future.Ministerio de Ciencia e Innovación TIN2008-04487-EMinisterio de Ciencia e Innovación TIN-2009-13192Junta de Andalucía P08-TIC-04200Ministerio de Educación y Ciencia MTM2009-12716Junta de Andalucía PO6-TIC-02268Universidad del País Vasco EHU09/0

Segmenting images with gradient-based edge detection using Membrane Computing

In this paper, we present a parallel implementation of a new algorithm for segmenting images with gradient-based edge detection by using techniques from Natural Computing. This bio-inspired parallel algorithm has been implemented in a novel device architecture called CUDA™(Compute Unified Device Architecture). The implementation has been designed via tissue P systems on the framework of Membrane Computing. Some examples and experimental results are also presented.Ministerio de Ciencia e Innovación TIN2008-04487-EMinisterio de Ciencia e Innovación TIN2009–13192Junta de Andalucía P08–TIC-04200Junta de Andalucía P06-TIC-02268Ministerio de Educación y Ciencia MTM2009-12716Universidad del Pais Vasco EHU09/0

Smoothing Problem in 2D Images with Tissue-like P Systems and Parallel Implementation

Smoothing is often used in Digital Imagery to reduce noise within an image. In this paper we present a Membrane Computing algorithm for smoothing 2D images in the framework of tissue-like P systems. The algorithm has been implemented by using a novel device architecture called CUDATM, (Compute Unified Device Architecture). We present some examples, compare the obtained time and present some research lines for the future.Ministerio de Ciencia e Innovación TIN2008-04487-EMinisterio de Ciencia e Innovación TIN-2009-13192Junta de Andalucía P08-TIC-04200Junta de Andalucía PO6-TIC-02268Ministerio de Educación y Ciencia MTM2009-1271

Fully automatized parallel segmentation of the optic disc in retinal fundus images

This paper presents a fully automatic parallel software for the localization of the optic disc (OD) in retinal fundus color images. A new method has been implemented with the Graphics Processing Units (GPU) technology. Image edges are extracted using a new operator, called AGP-color segmentator. The resulting image is binarized with Hamadani’s technique and, finally, a new algorithm called Hough circle cloud is applied for the detection of the OD. The reliability of the tool has been tested with 129 images from the public databases DRIVE and DIARETDB1 obtaining an average accuracy of 99.6% and a mean consumed time per image of 7.6 and 16.3 s respectively. A comparison with several state-of-the-art algorithms shows that our algorithm represents a significant improvement in terms of accuracy and efficiency.Ministerio de Economía y Competitividad TIN2012-3743

Studying the Chlorophyll Fluorescence in Cyanobacteria with Membrane Computing Techniques

In this paper, we report a pioneer study of the decrease in chlorophyll uorescence produced by the reduction of MTT (a dimethyl thiazolyl diphenyl tetrazolium salt) monitored using an epi uorescence microscope coupled to automate image analysis in the framework of P systems. Such analysis has been performed by a family of tissue P systems working on the images as data inpuJunta de Andalucía P08-TIC-04200Ministerio de Economía y Competitividad TIN2012-3743

Antimatter as a Frontier of Tractability in Membrane Computing

Summary. It is well known that the polynomial complexity class of recognizer polarizationless P systems with active membranes, without dissolution and with division for elementary and non-elementary membranes is exactly the complexity class P (see [6], Th. 2). In this paper, we prove that if such P system model is endowed with antimatter and annihilation rules, then NP problems can be solved. In this way, antimatter is a frontier of tractability in Membrane Computing.