使用 SIFT 计算图像与其旋转版本之间的相似度
我已经在opencv中实现了SIFT来比较图像...我还没有编写用于比较的程序。考虑使用FLANN进行相同的处理。但是，我的问题是，查看描述符的128个元素，无法真正理解图像与其旋转版本的相似性。
通过阅读 Lowe 的论文，我确实了解到描述符坐标都是根据关键点方向进行旋转的……但是，相似度到底是如何获得的。我们可以通过查看 128 个值来了解相似度吗？
请帮助我...这是我的项目演示。
I have implemented SIFT in opencv for comparing images... i have not yet written the program for comparing.Thinking of using FLANN for the same.But,my problem is that,looking into the 128 elements of the descriptor,cannot really understand the similarity of an image and its rotated version.
By reading Lowe's paper,i do understand that the descriptor co-ordinates are all rotated in terms of the keypoint orientation...but,how exactly is the similarity obtained.Can we undertstand the similarity by just viewing the 128 values.
pls,help me...this is for my project presentation.
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您可以首先使用 Lowe 度量来计算两个图像之间的一些假定匹配。度量是对于图像 1 中的任何给定描述符 de，找到到图像 2 中所有描述符 de' 的距离。如果最近距离与第二最近距离的比率低于阈值，则接受它。
之后，您可以执行 RANSAC 或其他形式的稳健估计或霍夫变换，以检查您接受为假定匹配的关键点的位置、方向和比例方面的几何一致性。
You can first use Lowe's metric to compute some putative matches between the two images. The metric is that for any given descriptor de in image 1, find the distance to all descriptors de' in image 2. If the ratio of the closest distance to the second closest distance is below a threshold, then accept it.
After this, you can do RANSAC or other form of robust estimation or Hough Transform to check geometric consistency in terms of position, orientation, and scale of the keypoints that you accepted as putative matches.
如果我没记错的话，SIFT 会给你一组 128 个值的描述符来描述每个兴趣点。您还可以了解每个图像中每个点的位置，以及每个图像中的“方向”（我忘记了“方向”在论文中被称为什么）和比例。
找到具有匹配描述符的两个点后，您可以通过比较坐标和方向来计算从一张图像中的兴趣点到另一张图像中的相同点的变换。
如果您有足够的匹配，您会看到所有（或大多数）兴趣点是否具有相同的转换。如果存在，则图像相似，如果不存在，则图像不同。
希望这有帮助...
If I recall correctly, SIFT will give you a set of 128-value descriptors that describe each of the interest points. You also have the location of each point in each of the images, as well as its "direction" (I forget what the "direction" is called in the paper) and scale in each image.
Once you've found two points that have matching descriptors, you can calculate the transformation from the interest point in one image to the same point in the other image by comparing coordinates and directions.
If you have enough matches, you see if all (or a majority of) the interest points have the same transformation. If they do, the images are similar, if they don't, the images are different.
Hope this helps...
您正在寻找的基本上是 ASIFT
您可以在此处找到代码和一些概述< /a>
What you are looking for is basically ASIFT
You can find the code here and some overview