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.