stitching microscope images of a microchip

Question

So, I'm trying to stitch images taken by a microscope of a microchip, but it's very hard to have all the features aligned. I already have a 50% overlap between two adjacent images, but even with that, it's not always a good fit.

I'm using SURF with OpenCV to extract the keypoints and find the homographic matrix. But still, it's far from being an acceptable result.

My objective is to be able to stitch perfectly 2x2 images, so this way, I can repeat that process recursively until I have the final image.

Do you have any suggestion ? A nice algorithm to approach this problem. Or maybe a way to transform the images to be able to extract better keypoints from them. Play with the threshold (a smaller one to get more keypoints, or a larger one?).

Right now, my approach is to first stitch two 2x1 images and then, stitch these two together. It's close from what we want, but still not acceptable. Also, the problem might be that the image used to be the "source" (while the second image is transform with the matrix to overlap that one) might not be a bit misaligned or there's a small angle on that image that affects the whole result.

Any help or suggestion is appreciated. Specially any solution that would allow to use OpenCV and SURF (even if I'm not totally against other libraries... it's just that most of the project has been developed with that).

Thanks!

Answer 1

I found using TurboReg during image registration development to be a helpful comparison tool. It is a free ImageJ plugin, and has many different fitting types.

Have you taken a look at the new OpenCV stitching samples: stitching.cpp and stitching_detailed.cpp?

EDIT : I forgot this was cutting edge OpenCV because I'm using the trunk at home :) To get access to these new samples, you'll need to check out the OpenCV trunk from SVN like this:

svn co https://code.ros.org/svn/opencv/trunk/opencv opencv-trunk

Unfortunately, you'll need to recompile it, but you should be able to use the new stitching code :) If you haven't built OpenCV from source before, here is a good little tutorial to get you started. I will mention that OpenCV has a lot more options that can be enabled/disabled than are mentioned in the tutorial, so you might want to use the cmake-gui to get a look at all of the options. You can apt-get it with this command:

> sudo apt-get install cmake-qt-gui

Also, if you're more concerned with quality, and you don't mind slower performance; you might consider using the Lucas-Kanade method for image registration. Here is a lecture, and here is a paper on the topic that might be helpful to you.