Lucas-Kanade 20 years on: A unifying framework
Lucas-Kanade 20 years on: A unifying framework
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DOI：
10.1023/b:visi.0000011205.11775.fd
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发表时间：
2004-02-01
影响因子：
19.5
通讯作者：
Matthews, I
中科院分区：
文献类型：
--
作者：
Baker, S;Matthews, I
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。应用范围从光流和跟踪到分层运动、马赛克构造和人脸编码。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。我们研究哪些扩展卢卡斯-卡纳德可以使用逆合成算法没有任何显着的效率损失，而不能。在本文中，在一系列论文的第1部分，我们涵盖了数量近似，翘曲更新规则和梯度下降近似。在未来的论文中，我们将介绍误差函数的选择，如何允许线性外观变化，以及如何对参数施加先验。
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 and tracking to layered motion, mosaic construction, 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 Lucas-Kanade can be used with the inverse compositional algorithm without any significant loss of efficiency, and which cannot. In this paper, Part 1 in a series of papers, we cover the quantity approximated, the warp update rule, and the gradient descent approximation. In future papers, we will cover the choice of the error function, how to allow linear appearance variation, and how to impose priors on the parameters.