A parallel algorithm for skeletonizing images by using spiking neural P systems Daniel Dı ´az-Pernil a,n , Francisco Pen ˜ a-Cantillana b , Miguel A. Gutie ´ rrez-Naranjo b a CATAM Research Group – Department of Applied Mathematics I University of Seville, Spain b Research Group on Natural Computing – Department of Computer Science and AI, University of Seville, Spain Keywords: Spiking neural P systems Skeletonizing Membrane computing Guo and Hall algorithm abstract 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. 1. Introduction Computer vision [1] is probably one of the most promising challenges for computer scientists in the near future. This grow- ing research area needs contributions from many other scientific fields such as artificial intelligence, pattern recognition, signal processing, neurobiology, psychology or image processing. Com- puter vision deals with the automated processing of images from the real world in order to extract and interpret pieces of information. Under a computational point of view, a digital image is a function from a two-dimensional surface; each point in the surface is mapped into a set of features such as brightness or colour. The computational treatment of such mappings (or digital images) is the basis of many current applications in computer vision such as optical character recognition (OCR), biometrics, automotive safety or surveillance. In this paper, we focus on the problem of skeletonizing an image. Skeletonization is one of the approaches for representing a shape with a small amount of information by converting the initial image into a more compact representation and keeping the meaning features. The conversion should remove redundant information, but it should also keep the basic structure. Skeletonization is usually considered as a pre-process in pattern recognition algorithms, but its study is also interesting by itself for the analysis of line-based images as texts, line drawings, human fingerprints or cartography. Many problems in the processing of digital images have features which make it suitable for techniques inspired by nature. One of them is that the treatment of the image can be parallelized and locally solved. Regardless how large is the image, the process can be performed in parallel in different local areas of it. Another interesting feature is that the local information needed for a pixel transformation can also be easily encoded in the data structures used in natural computing. In the literature, we can find many examples of the use of natural computing techniques for dealing with problems associated to the treatment of digital images. One of the classic examples is the use of cellular automata [2]. Other efforts are related to artificial neural networks as in [3]. In this paper, we use spiking neural P systems, a computational model in the framework of membrane computing. Spiking neural P systems (SN P systems, for short) were introduced in [4] as a new class of distributed and parallel computing devices, inspired by the neurophysiological behavior of neurons sending electrical impulses (spikes) along axons to other neurons. SN P systems are the third model of computation in the framework of membrane computing, 1 together with the cell-like model inspired by the compartmental structure and functioning of a living cell and the tissue-like model, based on n Corresponding author. Tel.: þ34 95 455 69 21. E-mail addresses: sbdani@us.es (D. Dı ´az-Pernil), frapencan@gmail.com (F. Pen ˜ a-Cantillana), magutier@us.es (M.A. Gutie ´ rrez-Naranjo). 1 We refer to [5] for a comprehensive presentation in this area and the P system web page http://ppage.psystems.eu, for the up-to-date information.