Yang Gehua, Stewart Charles V, Sofka Michal, Tsai Chia-Ling
Department of Computer Science, Rensselaer Polytechnic Institute, 110 18th St, Troy, NY 12180, USA.
IEEE Trans Pattern Anal Mach Intell. 2007 Nov;29(11):1973-89. doi: 10.1109/TPAMI.2007.1116.
Our goal is an automated 2D-image-pair registration algorithm capable of aligning images taken of a wide variety of natural and man-made scenes as well as many medical images. The algorithm should handle low overlap, substantial orientation and scale differences, large illumination variations, and physical changes in the scene. An important component of this is the ability to automatically reject pairs that have no overlap or have too many differences to be aligned well. We propose a complete algorithm, including techniques for initialization, for estimating transformation parameters, and for automatically deciding if an estimate is correct. Keypoints extracted and matched between images are used to generate initial similarity transform estimates, each accurate over a small region. These initial estimates are rank-ordered and tested individually in succession. Each estimate is refined using the Dual-Bootstrap ICP algorithm, driven by matching of multiscale features. A three-part decision criteria, combining measurements of alignment accuracy, stability in the estimate, and consistency in the constraints, determines whether the refined transformation estimate is accepted as correct. Experimental results on a data set of 22 challenging image pairs show that the algorithm effectively aligns 19 of the 22 pairs and rejects 99.8% of the misalignments that occur when all possible pairs are tried. The algorithm substantially out-performs algorithms based on keypoint matching alone.
我们的目标是开发一种自动化的二维图像对配准算法，该算法能够对齐拍摄的各种自然和人造场景的图像以及许多医学图像。该算法应能处理低重叠度、显著的方向和比例差异、较大的光照变化以及场景中的物理变化。其中一个重要组成部分是能够自动剔除没有重叠或差异过大而无法良好对齐的图像对。我们提出了一种完整的算法，包括初始化技术、估计变换参数的技术以及自动判定估计是否正确的技术。在图像之间提取并匹配的关键点用于生成初始相似性变换估计，每个估计在一个小区域内是准确的。这些初始估计按秩排序并依次单独测试。每个估计使用双引导迭代最近点（ICP）算法进行细化，该算法由多尺度特征的匹配驱动。一个由对齐精度测量、估计稳定性和约束一致性组成的三部分决策标准，决定了细化后的变换估计是否被接受为正确。对一组包含22对具有挑战性的图像的数据集进行的实验结果表明，该算法有效地对齐了22对中的19对，并在尝试所有可能的图像对时剔除了99.8%的未对齐情况。该算法显著优于仅基于关键点匹配的算法。
