# How do I register or stitch multiple sub-images together into a larger image using unique fiducial features with rigid transformation?

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gwoo 2023년 1월 13일
댓글: Cris LaPierre 2023년 1월 14일
I have 5 sub images that are unique. Each image contains at least 2 unique fiducial features that are only shared by a pair of images.
I would like to stitch/register the 4 satellite images onto the center image.
I have tried using the Image Processing/ Computer Vision tools, functions, and even panorama stitching walk through and the results fail every time. It doesn't fit the features together well at all. Incorrect identifications or mapping.
How can I combine these images, using only translation and rotation, to create a larger single image with solid alignment?
Example of attached images:
Example of attached desired result (made in photoshop):

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Image Analyst 2023년 1월 13일
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William Rose 2023년 1월 13일
Use xcorr2(). See the example here:
Determine the best alignment of image 1 and image 2 with xcorr2(). Make a bigger image, let's call it u12, consisting of the union of images 1 and 2. Then do the cross correlation of u12 with image3, to determine their best alignment. Use this information to make u123. Continue this process with image 4 and image 5. As your union image gets larger, xcorr2() will take longer and use even more memory.
Good luck!
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William Rose 2023년 1월 13일
You are right that xcorr2() will only help find a translation, and will not help with rotations. Your question implies that the image scales are the same in all the images (because you did not ask about scal e adjustments) and that you don't have to worry about projection issues of 3D world onto 2D image plane.
If you can identify the same 3 world points in two images (which will have different coordinates in each image) then you can determine the translation and rotation that will make the world points coincide. Even doing "just that" is not totally trivial.
Sounds interesting but hard. Good luck!

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Cris LaPierre 2023년 1월 13일
편집: Cris LaPierre 2023년 1월 13일
You would probably be intersted in the Computer Vision for Engineering and Science Specialization from MathWorks on Coursera. You can enroll for free, and the first course covers aligning and stitching images together.
Just keep in mind that image registration does not 'see' shapes and objects. Instead, it sees corners and edges. It will likely take some trial and error, as well as some educating yourself on the strengths of the different algorithms. You'll find examples with working code in the course I linked to that can serve as great starting points.
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Cris LaPierre 2023년 1월 14일
If you are open to a more manual approach, you might be interested in cpselect, which allows you to manually select control points between 2 images.

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