An efficient implementation of fuzzy edge detection using GPU in MATLAB

Abstract

Edge detection is one of the most important concepts in image processing which is used as an indicator for processing and extraction of some of border characteristics at low levels, also for detection and finding objects at high levels. Due to the inherently parallel nature of edge detection algorithms, they suit well for implementation on a Graphics Processing Unit (GPU). First part of this paper aims to detect and retouch image edges using fuzzy inference system. In the first step RGB images converted to gray scale images. In the second step the input images are converted from unit 8 class to double class. In the third step, fuzzy inference system is defined with two inputs. Fuzzy inference system rules and membership function are applied on these two inputs. The output with black pixels indicates areas with edge and the output with white pixels indicates areas without edge. The second part of this paper, the performance of fuzzy edge detection algorithm is improved using GPU platform by exploiting data-level parallelism and scatter/gather parallel communication pattern in Matlab environment. The experimental results show that the performance is improved for different image sizes of up to 11.8x.