I'm glad it was helpful!

Thank you for your comments! For sure, the CNN is expressive for learning the characteristics of images. However, in this development, I tried to not use deep-learning because I believe that it is important to provide fast, consistent results without the need for training data. If you are particularly interested in this app, I would be glad if you could create a pull request to extend the algorithm.

The parent comment said nothing about using deep learning. Convolution is not the same as using a CNN. I interpreted their comment as meaning they used a 2D convolution (presumably a 2D cross correlation, actually) to find regions of overlap

Yes you're right it was a 2D cross-correlation which is very analogous to a convolution

If memory serves… the only difference is that one of the kernels being convolved is reversed for convolution.

Thank you for comments! Certainly, this application may not be able to handle any kinds of images. However, I tried to stitch images without using deep-learning. Therefore, the strength of this app is that when this app receives the same images, it always produces consistent results. In the future, I will try to develop a more effective image merging method in more generalized scenario.

Is deep learning state of the art for something like this?

Would have expected it to just be kernel based.

Regardless, you can have fully deterministic deep learning approaches. You can use integers, run on a CPU, and seed everything.