Discrete rigid registration: A local graph-search approach
Discrete rigid registration: A local graph-search approach
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DOI：
10.1016/j.dam.2016.05.005
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发表时间：
2017-01
期刊：
影响因子：
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通讯作者：
Phuc Ngo;Y. Kenmochi;A. Sugimoto;Hugues Talbot;Nicolas Passat
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文献类型：
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作者：
Phuc Ngo;Y. Kenmochi;A. Sugimoto;Hugues Talbot;Nicolas Passat
图像配准已经成为从计算机视觉到计算机图形学的广泛成像领域中的关键步骤。图像配准的核心是确定两幅图像之间的最佳映射变换。这个问题是不适定的;由于图像的大尺寸和变换参数空间的高维数，也难以处理。计算一个真正的最佳解决方案是几乎不可能的变换时，假设连续（即，定义在R n）。在这篇文章中，我们开始探索一种新的方式考虑图像配准。由于数字图像基本上定义在一个离散的框架（即，在Z n），变换空间，尽管潜在的高复杂性，实际上仍然是有限的，允许通过离散优化方案的参数空间的显式探索的发展。我们提出了一个分析注册的基础上，通过考虑2D图像之间的刚性配准。我们显示，特别是，如何处理这个问题可以在一个完全离散的方式，通过计算本地的离散刚性变换的参数空间的组合结构，并通过导航在这个空间内的飞行通过梯度下降范式。这种配准框架适用于真实的成像情况下，强调我们的方法的相关性，其进一步扩展到更高维的图像和更丰富的变换的潜在有用性。
Image registration has become a crucial step in a wide range of imaging domains, from computer vision to computer graphics. The core of image registration consists of determining the transformation that induces the best mapping between two images. This problem is ill-posed; it is also difficult to handle, due to the high size of the images and the high dimension of the transformation parameter spaces. Computing an actually optimal solution is practically impossible when transformations are assumed continuous (ie, defined on R n). In this article, we initiate the exploration of a new way of considering image registration. Since digital images are basically defined in a discrete framework (ie, in Z n), the transformation spaces–despite a potentially high complexity–actually remain finite, allowing for the development of explicit exploration of the parameter space via discrete optimization schemes. We propose an analysis of the very basis of registration, by considering rigid registration between 2D images. We show, in particular, how this problem can be handled in a fully discrete fashion, by computing locally the combinatorial structure of the parameter space of discrete rigid transformations, and by navigating on-the-flight within this space via gradient descent paradigms. This registration framework is applied in real imaging cases, emphasizing the relevance of our approach, and the potential usefulness of its further extension to higher dimension images and richer transformations.