Improved particle swarm optimization for fast block matching with application to motion estimation in micro/nano systems

Gaozhao Su, Guoliang Lu, Peng Yan
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引用次数: 2

Abstract

The function of motion estimation (ME) is crucial in micro/nano systems in which the use of RGB/gray cameras attracts an increasing attention in recent years. In this paper, to improve the computational efficiency of existing PSO-based motion estimation method using RGB/gray cameras, we present a novel fast algorithm for the motion estimation problem with application to nano/micro manipulation systems. Particularly, in the proposed method, we improve the PSO with introducing the narrow solution space (NSS), to develop an improved version of PSO which was experimentally demonstrated significant improvements over the standard PSO on the computation speed meanwhile with a comparable estimation accuracy. Moreover, the method can also potentially allow to be integrated with a sub-pixel interpolation technique for the purpose of allowing for more advanced manipulation tasks.
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基于改进粒子群算法的快速块匹配及其在微纳系统运动估计中的应用
运动估计在微纳系统中起着至关重要的作用,近年来RGB/灰度相机的应用越来越受到人们的关注。为了提高现有基于pso的RGB/灰度相机运动估计方法的计算效率,提出了一种新的快速运动估计算法,并应用于纳米/微操作系统。在本文提出的方法中,我们通过引入窄解空间(NSS)对粒子群算法进行改进,开发出一种改进版本的粒子群算法,实验证明该算法在计算速度上比标准粒子群算法有显著提高,同时具有相当的估计精度。此外,该方法还可以潜在地允许与亚像素插值技术集成,以便允许更高级的操作任务。
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