如何识别同一对象的两个图像中的更改 [英] How to identify changes in two images of same object

问题描述

我有两个图像，我知道代表完全相同的对象。在下面的图片中，它们被称为参考和匹配。

I have two images which I know represent the exact same object. In the picture below, they are referred as Reference and Match.

与参考文献相比，图像Match可以进行以下转换：

The image Match can undergo the following transformations compared to Reference:

1. The object may have changed its appearance locally by addition(e.g. dirt or lettering added to the side) or omission (side mirror has been taken out).

水平拉伸或缩小尺寸（仅限于侧面镜子） （不在垂直方向调整大小）

Stretched or reduced in size horizontally only (it is not resized in vertical direction)

参考图像的部分不存在于匹配中（参考图像中以红色阴影显示）。

Portions of Reference image are not present in Match (shaded in red in Reference Image).

问题：如何识别以上述方式改变的地区？

Question: How can the regions which have "changed" in the ways mentioned above be identified ?

想法＃1：动态时间扭曲看起来像一个好的候选人，一旦匹配图像的开始和结束（图像中编号为1和3）参考图片，但我不知道该如何处理。

Idea#1: Dynamic Time Warping seems like a good candidate once the beginning and end of Match image (numbered 1 and 3 in the image) are aligned with corresponding columns in Reference Image, but I am not sure how to proceed.

想法2：在图片中匹配SIFT功能。由特征点位置产生的镶嵌将图像分成不均匀的瓦片。使用图像之间的特征对应关系来确定要在图像之间匹配的图块。使用相似性度量来计算任何更改。

Idea#2: Match SIFT features across images. The tessellation produced by feature point locations breaks up the image into non-uniform tiles. Use feature correspondences across images to determine which tiles to match across images. Use a similarity measure to figure out any changes.

推荐答案

您可能需要考虑一种迭代注册算法。基本上，您想要执行优化以查找变换的参数，在您的情况下水平缩放和水平平移。一旦你优化参数，你将有两个图像之间的转换，转换一个匹配另一个，然后可以使用减法来识别区域有差异。

You might want to consider an iterative registration algorithm. Basically you want to perform optimization to find the parameters of the transform, in your case horizontal scaling and horizontal translation. Once you optimize the parameters you will have the transformation between the two images, transform one to match the other, and can then use a subtraction to identify the regions with differences.

有关注册，请查看ITK库。
你可以使用互信息作为度量来做梯度体面优化。它有许多不同的转换，将捕获转换和缩放。代码应该对您显示的示例图片快速运行。

For registration take a look at the ITK library. You can probably do a gradient decent optimization using mutual information as the metric. It has a number of different transforms that will capture translation and scaling. The code should run quickly on the sample images you show.

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