IEEE Trans Image Process. 2015 Jul;24(7):2124-39. doi: 10.1109/TIP.2015.2409978. Epub 2015 Mar 4.
In this paper, we address the problem of fast and accurate extraction of points that correspond to the same location (named tie-points) from pairs of large-sized images. First, we conduct a theoretical analysis of the performance of the full-image matching approach, demonstrating its limitations when applied to large images. Subsequently, we introduce a novel technique to impose spatial constraints on the matching process without employing subsampled versions of the reference and the target image, which we name coupled image decomposition. This technique splits images into corresponding subimages through a process that is theoretically invariant to geometric transformations, additive noise, and global radiometric differences, as well as being robust to local changes. After presenting it, we demonstrate how coupled image decomposition can be used both for image registration and for automatic estimation of epipolar geometry. Finally, coupled image decomposition is tested on a data set consisting of several planetary images of different size, varying from less than one megapixel to several hundreds of megapixels. The reported experimental results, which includes comparison with full-image matching and state-of-the-art techniques, demonstrate the substantial computational cost reduction that can be achieved when matching large images through coupled decomposition, without at the same time compromising the overall matching accuracy.
在本文中，我们解决了从一对大型图像中快速准确地提取对应相同位置的点（称为同名点）的问题。首先，我们对全图匹配方法的性能进行了理论分析，证明了当应用于大型图像时，它存在局限性。随后，我们引入了一种新的技术，通过不使用参考图像和目标图像的子采样版本来对匹配过程施加空间约束，我们称之为耦合图像分解。该技术通过一个理论上对几何变换、加性噪声和全局辐射差异不变的过程将图像分割成相应的子图像，并且对局部变化具有鲁棒性。介绍之后，我们展示了如何将耦合图像分解用于图像配准和自动估计视差几何。最后，我们在由不同大小的多个行星图像组成的数据集上测试了耦合图像分解，这些图像的大小从不到一百万像素到几百个百万像素不等。报告的实验结果，包括与全图匹配和最先进技术的比较，证明了当通过耦合分解匹配大型图像时，可以显著降低计算成本，同时不会影响整体匹配精度。
