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

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.

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