MOE KLINNS Lab, Institute of Integrated Automation, School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
ONERA, The French Aerospace Lab, Chemin de la Hunière, F-91761 Palaiseau, France.
Sensors (Basel). 2019 Mar 4;19(5):1091. doi: 10.3390/s19051091.
Image registration is a crucial and fundamental problem in image processing and computer vision, which aims to align two or more images of the same scene acquired from different views or at different times. In image registration, since different keypoints (e.g., corners) or similarity measures might lead to different registration results, the selection of keypoint detection algorithms or similarity measures would bring uncertainty. These different keypoint detectors or similarity measures have their own pros and cons and can be jointly used to expect a better registration result. In this paper, the uncertainty caused by the selection of keypoint detector or similarity measure is addressed using the theory of belief functions, and image information at different levels are jointly used to achieve a more accurate image registration. Experimental results and related analyses show that our proposed algorithm can achieve more precise image registration results compared to several prevailing algorithms.
图像配准是图像处理和计算机视觉中的一个关键和基本问题，旨在对齐从不同视角或不同时间获取的同一场景的两个或多个图像。在图像配准中，由于不同的关键点（例如，角点）或相似性度量可能导致不同的配准结果，因此关键点检测算法或相似性度量的选择会带来不确定性。这些不同的关键点检测器或相似性度量各有优缺点，可以共同使用，以期望获得更好的配准结果。在本文中，使用信任函数理论来处理由关键点检测器或相似性度量的选择引起的不确定性，并联合使用不同层次的图像信息来实现更准确的图像配准。实验结果和相关分析表明，与几种现有算法相比，我们提出的算法可以实现更精确的图像配准结果。
