GPGPU Implementation of a Genetic Algorithm for Stereo Refinement

Abstract

-During the last decade, the general-purpose computing on graphics processing units Graphics (GPGPU) has turned out to be a useful tool for speeding up many scientific calculations. Computer vision is known to be one of the fields with more penetration of these new techniques. This paper explores the advantages of using GPGPU implementation to speedup a genetic algorithm used for stereo refinement.The main contribution of this paper is analyzing which genetic operators take advantage of a parallel approach and the description of an efficient state-of-the-art implementation for each one. As a result, speed-ups close to x80 can be achieved, demonstrating to be the only way of achieving close to real-time performance.

PI Stabilization for Congestion Control of AQM Routers with Tuning Parameter Optimization

“…In the first step of the process, the Initial Population in the Genetic Algorithm generates some random solutions. In the second step, the random value of Crossover aid to make new offspring and in the third step, with random value of Mutation let to change a few of gens [17] [22][23].…”

The Use of Genetic Algorithms in UV Disinfection of Drinking Water

“…Its basic principles were proposed by Holland [2], and are well described in several books, as Goldberg [3], Poli, Langdon, McPhee [4], Michelle [5] or Davis [6]. They had been used for Traffic Signal Timings Optimization [7], for transport management in docks [8], to improve the Production of Biochemical Pathway [9], for recognizing the position of Ezafe construction in Persian Texts [10], or even for GPGPU Implementation of a Genetic Algorithm for Stereo Refinement [11]. Just to name a few.…”