Learning OpenCV, 2nd Edition

6

General Image Transforms

Overview

In the previous chapter, we covered the class of image transformations that can be understood specifically

in terms of convolution. Of course, there are a lot of useful operations that cannot be expressed in this way

(i.e., as a little window scanning over the image doing one thing or another). In general, transformations

that can be expressed as convolutions are local, meaning that even though they may change the entire

image, the effect on any particular pixel is determined by only a small number of pixels around that one.

The transforms we will look in this chapter generally will not have this property.

Some very useful image transforms are very simple, and you will use them all of the time—resize for

example. Others are somewhat more special purpose. In many cases, the image transforms we will look at

in this chapter have the purpose of converting an image into some entirely different representation. This

different representation will usually still be an array of values, but those values might be quite different in

meaning than the intensity values in the input image. An example of this would be the frequency

representation resulting from a Fourier transform. In a few cases, the result of the transformation will be

something like a list of components, as would be the case for the Hough Line Transform.

There are a number of useful transforms that arise repeatedly in computer