Part 1
1.1: Finite Difference Operator
Partial derivatives in x.
Partial derivatives in y.
Binarized edge detection.
1.2: Derivative of Gaussian Filter
Partial derivatives in x (after a Gaussian blur).
Partial derivatives in y (after a Gaussian blur).
Binarized edge detection. I noticed that the partials produced more defined results, and the resulting edge detection created edges that appeared continuous, rather than 1.1's edge detection that looked more like a discrete set of points that more or less fit to an edge. It appears to be an overall superior method of edge detection compared to 1.1
1.3: Image Straightening
Original image
Straightened
Histogram
Original
Straigthened
Histogram
Original
Straigthened
Histogram
Original
Straigthened - Failure Case. Interesting that images with tons of detail and Fourier frequencies can straighten, but a simple plus sign can't with my algorithm.
Histogram
