Lucas-Kanade 20 Years On: A Unifying Framework: Part 2
Lucas-Kanade 20 Years On: A Unifying Framework: Part 2
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Lucas-Kanade 20 周年：统一框架：第 2 部分
DOI：
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发表时间：
2003
期刊：
影响因子：
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通讯作者：
I. Matthews
中科院分区：
文献类型：
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作者：
Simon Baker;Ralph Gross;Takahiro Ishikawa;I. Matthews
自1981年Lucas-Kanade算法提出以来，图像配准已成为计算机视觉中应用最广泛的技术之一。应用范围从光流，跟踪和分层运动，马赛克建设，医学图像配准和人脸编码。许多算法已被提出，并已作出了各种各样的扩展到原来的配方。我们提出了一个图像对齐的概述，在一个一致的框架中描述了大多数算法及其扩展。我们专注于逆合成算法，我们最近提出的一个有效的算法。我们研究的扩展卢卡斯-Kanade算法可以使用逆合成算法没有任何显着的效率损失，并需要额外的计算。在本文中，第2部分在一系列的文件，我们涵盖了选择的误差函数。我们首先考虑加权L2范数。然后我们考虑鲁棒误差函数。
Since the Lucas-Kanade algorithm was proposed in 1981, image alignment has become one of the most widely used techniques in computer vision. Applications range from optical flow, tracking and layered motion, to mosaic construction, medical image registration, and face coding. Numerous algorithms have been proposed and a wide variety of extensions have been made to the original formulation. We present an overview of image alignment, describing most of the algorithms and their extensions in a consistent framework. We concentrate on the inverse compositional algorithm, an efficient algorithm that we recently proposed. We examine which of the extensions to the Lucas-Kanade algorithm can be used with the inverse compositional algorithm without any significant loss of efficiency, and which require extra computation. In this paper, Part 2 in a series of papers, we cover the choice of the error function. We first consider weighted L2 norms. Afterwards we consider robust error functions.