A Study on Parallel and Pipelining Simulation Techniques for Edge Detection and Their Performance Analysis

Abstract

Real processing components along with component simulators are combined together to construct a new virtual prototyping system. The increase in component simulators result in degraded performance of the simulation in distributed systems. The speed of simulation can be increased by doing parallel simulation techniques. Prime number test and Image edge detection are chosen to implement the parallel simulation techniques and achieved the expected results while implementing in real time applications. The prime number test calculates the number of processors in a system and the image edge detection can be done in two stages by Canny Edge detection and Sobel Edge detection. The Canny Edge detection is used to detect the edges in the images by using a multi-stage algorithm. The smaller, separable and integer valued filter in images are combined in horizontal and vertical directions by using the Sobel edge detection resulting in reduction of implementation cost. The tool named OpenMP is used for implementing the parallel simulation techniques by combining both the canny edge and Sobel edge detection. An add-on named MPI is used along with the OpenMP to reduce the implementation time in parallel processing.

Optimizing support vector machine (SVM) by social spider optimization (SSO) for edge detection in colored images

“…Horvath et al 23 demonstrate significant speed improvements in the Canny operator by utilizing CUDA for parallel processing on GPUs. Similarly, Livingston et al 24 explore parallel processing techniques to enhance the speed of edge detection algorithms.…”