This chapter covers the methods to change an image into an alternate representation of data in order to cover important problems of computer vision and image processing. Some examples of these methods are artifacts that are used to find image edges as well as transforms that help us find lines and circles in an image. In this chapter, we have covered stretch, shrink, warp, and rotate operations. A very useful and famous transform is Fourier, which transforms signals between the time domain and frequency domain. In OpenCV, you can find the **Discrete Fourier Transform** (**DFT**) and **Discrete Cosine Transform** (**DCT**). Another transform that we've covered in this chapter is related to integral images that allow rapid summing of sub regions, which is a very useful step in tracking faces algorithm. Besides this, you will also get to see distance transform and histogram equalization in this chapter.

We will cover the following topics:

Gradients and sobel derivatives

The Laplace...