Learning OpenCV, 2nd Edition

Applying this equalization process to

Figure 6-18 yields the equalized intensity distribution histogram and resulting image in Error! Reference

source not found..

Figure 6-21: Histogram equalized results: the spectrum has been spread out

cv::equalizeHist(), Contrast Equalization

OpenCV wraps this whole process up in one neat function.

void cv::equalizeHist(

const cv::InputArray src, // Input Image

cv::OutputArray dst // Result image

);

In cv::equalizeHist(), the source src must be a single-channel, 8-bit image. The destination image

dst will be the same. For color images you will have to separate the channels and process them one by

one.

Segmentation

The topic of image segmentation is a large one, which we have touched on in several places already, and

will return to in more sophisticated contexts later in the book as well. Here, we will focus on several

methods of the library that specifically implement techniques that are either segmentation methods in

themselves, or primitives that will be used later by more sophisticated tactics. It should be noted that at this

time, there is no general “magic” solution for image segmentation, and it remains a very active area in

computer vision research. Despite this, many good ...