CPU, GPU and FPGA Implementations of MALD: Ceramic Tile Surface Defects Detection Algorithm
Computer and Software Engineering Department, Faculty of Electrical Engineering, Josip Juraj Strossmayer University of Osijek, Kneza Trpimira 2B, 31000 Osijek, Croatia
Automatika – Journal for Control, Measurement, Electronics, Computing and Communications, Vol. 55 (1), 2014
@article{matic2014cpu,
title={CPU, GPU and FPGA Implementations of MALD: Ceramic Tile Surface Defects Detection Algorithm},
author={Matic, Tomislav and Aleksi, Ivan and Hocenski, {v{Z}}eljko},
journal={Automatika–Journal for Control, Measurement, Electronics, Computing and Communications},
volume={55},
number={1},
year={2014}
}
This paper addresses adjustments, implementation and performance comparison of the Moving Average with Local Difference (MALD) method for ceramic tile surface defects detection. Ceramic tile production process is completely autonomous, except the final stage where human eye is required for defects detection. Recent computational platform development and advances in machine vision provides us with several options for MALD algorithm implementation. In order to exploit the shortest execution time for ceramic tile production process, the MALD method is implemented on three different platforms: CPU, GPU and FPGA, and it is implemented on each platform in at least two ways. Implementations are done in MATLAB’s MEX/C++, C++, CUDA/C++, VHDL and Assembly programming languages. Execution times are measured and compared for different algorithms and their implementations on different computational platforms.
May 30, 2014 by hgpu
