This brings us to the end of our discourse on image derivatives and edge detection. We started off by discussing the concept of the derivatives of functions. Similar to some other mathematical concepts that we have covered (Gaussian functions), we saw that discrete approximation of the continuous derivatives can be applied to images. Image derivatives were a precursor to edge detection frameworks. We introduced a couple of different frameworks, namely Sobel and Canny. Toward the end of the chapter, we saw yet another technique that helps detect edge-like regions in images: the Laplacian (or the second derivative) operator. Apart from edge detection, Laplacian lends its utility to other related, practical use cases, such as quantifying the amount of blur in images.
As we progress through the book, you would notice a clear shift in our focus towards discussing processes that identify themselves as being core computer vision algorithms. You will realize, and perhaps you have started...
