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.

► Membrane Computing is inspired by the structure and functioning of cells. ► We present a bio-inspired operator (AGP segmentator) to do edge detection. ► The complexity of AGP segmentator is logarithmic with respect to the input data. ► We present a parallel implementation with CUDA based in our algorithm.