Keller Yosi, Averbuch Amir
Department of Mathematics, Yale University, New Haven, CT 06520-8283, USA.
IEEE Trans Pattern Anal Mach Intell. 2006 May;28(5):794-801. doi: 10.1109/TPAMI.2006.100.
This paper presents an approach to the registration of significantly dissimilar images, acquired by sensors of different modalities. A robust matching criterion is derived by aligning the locations of gradient maxima. The alignment is achieved by iteratively maximizing the magnitudes of the intensity gradients of a set of pixels in one of the images, where the set is initialized by the gradient maxima locations of the second image. No explicit similarity measure that uses the intensities of both images is used. The computation utilizes the full spatial information of the first image and the accuracy and robustness of the registration depend only on it. False matchings are detected and adaptively weighted using a directional similarity measure. By embedding the scheme in a "coarse to fine" formulation, we were able to estimate affine and projective global motions, even when the images were characterized by complex space varying intensity transformations. The scheme is especially suitable when one of the images is of considerably better quality than the other (noise, blur, etc.). We demonstrate these properties via experiments on real multisensor image sets.
本文提出了一种用于对由不同模态传感器获取的显著不同图像进行配准的方法。通过对齐梯度最大值的位置得出一种鲁棒的匹配准则。通过迭代最大化其中一幅图像中一组像素的强度梯度幅度来实现对齐，该组像素由另一幅图像的梯度最大值位置初始化。未使用同时利用两幅图像强度的显式相似性度量。计算利用了第一幅图像的完整空间信息，配准的准确性和鲁棒性仅取决于它。使用方向相似性度量来检测并自适应加权错误匹配。通过将该方案嵌入“从粗到精”的公式中，即使图像具有复杂的空间变化强度变换，我们也能够估计仿射和投影全局运动。当其中一幅图像的质量明显优于另一幅图像（噪声、模糊等）时，该方案特别适用。我们通过对真实多传感器图像集进行实验来证明这些特性。
