Part 1: Recover Homography Matrix
To recover the homography, I reshaped the matrices and did least squares to find the best fit h vector that transformed points P to poins P'. This was the method described in lecture.
Part 2: Warping the Images
I used an inverse warp to remove aliasing by interpolating from the original image. For each pixel position in the warped image, I use the inverse of H to find out where the pixel came from in the original image and interpolated from there. I check that the pixel is in the range of the original image, otherwise I set the value to 0.
Part 3: Image Rectification
For these images, I picked points that I knew were squares or rectangles in the images. And then I artificially create rectangle points simulating the warped points in the desired result. Then I found the homography and warped the image to align by those squares.
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Part 4: Mosaic
For these images, I picked points that I knew were squares or rectangles in the images. And then I artificially create rectangle points simulating the warped points in the desired result. Then I found the homography and warped the image to align by those squares.
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