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Full Description
Delve into practical computer vision and image processing projects and get up to speed with advanced object detection techniques and machine learning algorithms
Key Features
Discover best practices for engineering and maintaining OpenCV projects
Explore important deep learning tools for image classification
Understand basic image matrix formats and filters
Book DescriptionOpenCV 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.
This Learning Path is your guide to understanding OpenCV concepts and algorithms through real-world examples and activities. Through various projects, you'll also discover how to use complex computer vision and machine learning algorithms and face detection to extract the maximum amount of information from images and videos. In later chapters, you'll learn to enhance your videos and images with optical flow analysis and background subtraction. Sections in the Learning Path will help you get to grips with text segmentation and recognition, in addition to guiding you through the basics of the new and improved deep learning modules. By the end of this Learning Path, you will have mastered commonly used computer vision techniques to build OpenCV projects from scratch. This Learning Path includes content from the following Packt books:
Mastering OpenCV 4 - Third Edition by Roy Shilkrot and David Millán Escrivá
Learn OpenCV 4 By Building Projects - Second Edition by David Millán Escrivá, Vinícius G. Mendonça, and Prateek Joshi
What you will learn
Stay up-to-date with algorithmic design approaches for complex computer vision tasks
Work with OpenCV's most up-to-date API through various projects
Understand 3D scene reconstruction and Structure from Motion (SfM)
Study camera calibration and overlay augmented reality (AR) using the ArUco module
Create CMake scripts to compile your C++ application
Explore segmentation and feature extraction techniques
Remove backgrounds from static scenes to identify moving objects for surveillance
Work with new OpenCV functions to detect and recognize text with Tesseract
Who this book is forIf you are a software developer with a basic understanding of computer vision and image processing and want to develop interesting computer vision applications with OpenCV, this Learning Path is for you. Prior knowledge of C++ and familiarity with mathematical concepts will help you better understand the concepts in this Learning Path.
Contents
Table of Contents
Getting Started with OpenCV
An Introduction to the Basics of OpenCV
Learning Graphical User Interfaces
Delving into Histogram and Filters
Automated Optical Inspection, Object Segmentation, and Detection
Learning Object Classification
Detecting Face Parts and Overlaying Masks
Video Surveillance, Background Modeling, and Morphological Operations
Learning Object Tracking
Developing Segmentation Algorithms for Text Recognition
Text Recognition with Tesseract
Deep Learning with OpenCV
Cartoonifier and Skin Color Analysis on the RaspberryPi
Explore Structure from Motion with the SfM Module
Face Landmark and Pose with the Face Module
Number Plate Recognition with Deep Convolutional Networks
Face Detection and Recognition with the DNN Module
Android Camera Calibration and AR Using the ArUco Module
iOS Panoramas with the Stitching Module
Finding the Best OpenCV Algorithm for the Job
Avoiding Common Pitfalls in OpenCV
