So i've done some more work on it. I took out the homography estimation stuff because there's no point in using that on a flat image, it actually warps and worsens the result. The homography is great for panoramic images but is useless for flat images such as video game maps. The program now detects corners (Harris detection with Accord.NET still), correlates them, and instead of homography and ransac analysis to fit the inliers, it just subtracts the correlation points, then finds the majority of where the corners want to go and ignores the rest.
The result is a non-warped image that aligns perfectly, computes faster, and will work with any size image. The next thing to do is implement a batch process so i can open a directory of images, and optimize stuff here and there.
The Harris detection is kind of expensive for large pictures so either i reduce the area searched, or move to a more simple detection algorithm (which is possible because there's no noise with these images), this would require me to actually write the algorithm though.