整理目標：
（1） what is image registration ? ----- 定義 背景 應用（same/different scene ; multi/single model image; 3D/2D image）
（2）recent work in registration ？------ 相關的工作
（1）background：
Image registration： the process of overlaying two or more images of the same scene taken at different times from different viewpoints, and/or by different sensors. It geometrically aligns two images—the reference and sensed images
中文釋義：圖像配准是使用某種方法，基於某種評估標准，將一副或多副圖片（局部）最優映射到目標圖片上的方法。
附錄———相近概念類比
Dense image correspondence: correspondence estimation is a task of matching pixels of one image with those of others; when referring to dense correspondence estimation,the emphasis is on finding suitable matches(correspondences) for every one those pixels;
（是圖像配准的一個具體子目錄？應用場景不同？）
（2）Method：
基於特征的圖像配准方法
Feature detection：
Salient and distinctive objects(closed-boundary regions, edges, contours, line intersections, corners, etc.) are manually or, preferably, automatically detected. For further processing, these features can be represented by their point representatives (centers
of gravity, line endings, distinctive points), which are called control points (CPs) in the literature.
Feature matching：
In this step, the correspondence between the features detected in the sensed image and those detected in the reference image is established.
Various feature descriptors and similarity measures along with spatial relationships among the features are used for that purpose
Transform model estimation.
The type and parameters of the so-called mapping functions, aligning the sensed image with the reference image, are estimated. The parameters of the mapping functions are computed by means of the established feature correspondence.
Image resampling and transformation
the sensed image is transformed by means of the mapping functions.image values in non-integer coordinates are computed by the appropriate interpolation technique.
參考文獻：
【1】Armin M A, Barnes N, Khan S, et al. Unsupervised Learning of Endoscopy Video Frames’ Correspondences from Global and Local Transformation[M]//OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis. Springer, Cham, 2018: 108-117.
【2】知乎專欄 https://zhuanlan.zhihu.com/p/62210477
【3】Image registration methods: a survey