In the previous recipe, we showed you how the contrast of an image can be improved by stretching a histogram so that it occupies the full range of the available intensity values. This strategy indeed constitutes an easy fix that can effectively improve an image. However, in many cases, the visual deficiency of an image is not that it uses a too-narrow range of intensities. Rather, it is that some intensity values are used more frequently than others. The histogram shown in the first recipe of this chapter is a good example of this phenomenon. The middle-gray intensities are indeed heavily represented, while darker and brighter pixel values are rather rare. In fact, you would think that a good-quality image should make equal use of all available pixel intensities. This is the idea behind the concept of histogram equalization, that is, making the image histogram as flat as possible.
OpenCV Computer Vision Application Programming Cookbook Second Edition
By :
OpenCV Computer Vision Application Programming Cookbook Second Edition
By:
Overview of this book
Table of Contents (18 chapters)
OpenCV Computer Vision Application Programming Cookbook Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
Playing with Images
Manipulating Pixels
Processing Color Images with Classes
Counting the Pixels with Histograms
Transforming Images with Morphological Operations
Filtering the Images
Extracting Lines, Contours, and Components
Detecting Interest Points
Describing and Matching Interest Points
Estimating Projective Relations in Images
Processing Video Sequences
Index
Customer Reviews