一种深度图像的混合[ICP和GA]配准算法

Sagar Agarwal, Ishan Sharma, Anudeep Varma, A. Raj
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引用次数: 2

摘要

迭代最近点(ICP)是一种用于寻找旋转和平移的算法,以有效地配准两个点集。ICP算法的一个主要缺点是它要求在应用之前对数据点集进行近似注册。遗传算法为该问题提供了全局解,但不具有全局解的前提条件,但其收敛速度较慢。在本文中,我们演示了使用二进制遗传算法(GA)和综合ICP (CICP)算法(ICP算法的现有变体)的混合方法来配准人脸的深度图像。结果表明,遗传算法和CICP算法的应用具有快速、高效、不需要初始配准的特点。混合算法能够在平均2.0187秒内注册20个控制点,均方根误差为0.6148)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A hybrid [ICP and GA] image registration algorithm for depth images
Iterative Closest Point (ICP) is an algorithm used to find the rotation and translation to efficiently register two point sets. A major drawback of the ICP algorithm is that it demands the data point sets to be approximately registered before it can be applied. Genetic algorithms (GA) provide a global solution to this problem and have no such prerequisite, but their convergence speed is slow. In this paper, we have demonstrated the use of a hybrid of a binary genetic algorithm (GA) and the Comprehensive ICP (CICP) algorithm (an existing variant of the ICP algorithm) to register depth images of a human face. The application of the GA followed by the CICP algorithm has proven to be fast and efficient and has no precondition on initial registration. The hybrid algorithm was able to register twenty control points to an RMS error of 0.6148 in 2.0187 seconds on an average.).
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