Learn OpenCV 4 by Building Projects: Build real-world computer vision and image processing applications with OpenCV and C++,2nd Edition
Authors: David Millan Escriva – Vinicius G. Mendonca – Prateek Joshi
ISBN-10: 1789341221
ISBN-13: 9781789341225
Publication Date 出版日期: 2018-11-30
Print Length 页数: 310 pages
Book Description
By finelybook
Explore OpenCV 4 to create visually appealing cross-platform computer vision applications
OpenCV is one of the best open source libraries available,and can help you focus on constructing complete projects on image processing,motion detection,and image segmentation. Whether you’re completely new to computer vision,or have a basic understanding of its concepts,Learn OpenCV 4 by Building Projects – Second edition will be your guide to understanding OpenCV concepts and algorithms through real-world examples and projects.
You’ll begin with the installation of OpenCV and the basics of image processing. Then,you’ll cover user interfaces and get deeper into image processing. As you progress through the book,you’ll learn complex computer vision algorithms and explore machine learning and face detection. The book then guides you in creating optical flow video analysis and background subtraction in complex scenes. In the concluding chapters,you’ll also learn about text segmentation and recognition and understand the basics of the new and improved deep learning module.
By the end of this book,you’ll be familiar with the basics of Open CV,such as matrix operations,filters,and histograms,and you’ll have mastered commonly used computer vision techniques to build OpenCV projects from scratch.
What you will learn
Install OpenCV 4 on your operating system
Create CMake scripts to compile your C++ application
Understand basic image matrix formats and filters
Explore segmentation and feature extraction techniques
Remove backgrounds from static scenes to identify moving objects for surveillance
Employ various techniques to track objects in a live video
Work with new OpenCV functions for text detection and recognition with Tesseract
Get acquainted with important deep learning tools for image classification
contents
1 Getting Started with OpenCV
2 An Introduction to the Basics of OpenCV
3 Learning Graphical User Interfaces
4 Delving into Histogram and Filters
5 Automated Optical Inspection,Object Segmentation,and Detection
6 Learning Object Classification
7 Detecting Face Parts and Overlaying Masks
8 Video Surveillance,Background Modeling,and Morphological Operations
9 Learning Object Tracking
10 Developing Segmentation Algorithms for Text Recognition
11 Text Recognition with Tesseract
12 Deep Learning with OpenCV
