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Metric-Based Pairwise and Multiple Image Registration

机译:基于度量的成对和多个图像配准

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Registering pairs or groups of images is a widely-studied problem that has seen a variety of solutions in recent years. Most of these solutions are variations!, using objective functions that should satisfy several basic and desired properties. In this paper, we pursue two additional properties - (1) invariance of objective function under identical warping of input images and (2) the objective function induces a proper metric on the set of equivalence classes of images - and motivate their importance. Then, a registration framework that satisfies these properties, using the L~2-norm between a novel representation of images, is introduced. Additionally, for multiple images, the induced metric enables us to compute a mean image, or a template, and perform joint registration. We demonstrate this framework using examples from a variety of image types and compare performances with some recent methods.
机译:注册成对或图像组是近年来各种解决方案的广泛研究的问题。大多数这些解决方案是变体!,使用应满足几个基本和所需属性的客观函数。在本文中,我们追求了两个额外的属性 - (1)目标函数在输入图像相同翘曲下的目标函数的不变性,(2)目标函数在图像集的等价类别集合上引起适当的度量 - 并激励他们的重要性。然后,引入了使用在图像的新颖表示之间使用L〜2-NOM的满足这些属性的注册框架。另外,对于多个图像,诱导度量使我们能够计算平均图像或模板,并执行联合注册。我们使用来自各种图像类型的示例和使用一些最近的方法进行比较的框架来演示此框架。

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