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 that was 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 can 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...
OpenCV 4 Computer Vision Application Programming Cookbook - Fourth Edition
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OpenCV 4 Computer Vision Application Programming Cookbook - Fourth Edition
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Overview of this book
OpenCV is an image and video processing library used for all types of image and video analysis. Throughout the book, you'll work with recipes to implement a variety of tasks. With 70 self-contained tutorials, this book examines common pain points and best practices for computer vision (CV) developers. Each recipe addresses a specific problem and offers a proven, best-practice solution with insights into how it works, so that you can copy the code and configuration files and modify them to suit your needs.
This book begins by guiding you through setting up OpenCV, and explaining how to manipulate pixels. You'll understand how you can process images with classes and count pixels with histograms. You'll also learn detecting, describing, and matching interest points. As you advance through the chapters, you'll get to grips with estimating projective relations in images, reconstructing 3D scenes, processing video sequences, and tracking visual motion. In the final chapters, you'll cover deep learning concepts such as face and object detection.
By the end of this book, you'll have the skills you need to confidently implement a range of computer vision algorithms to meet the technical requirements of your complex CV projects.
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Current Title:
OpenCV 4 Computer Vision Application Programming Cookbook - Fourth Edition
Table of Contents (17 chapters)
Preface
Free Chapter
Playing with Images
Manipulating the 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
Reconstructing 3D Scenes
Processing Video Sequences
Tracking Visual Motion
Learning from Examples
OpenCV Advanced Features
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