What this book covers

Chapter 1, Getting Started with OpenCV, covers installation steps on various operating systems and provides an introduction to the human visual system as well as various topics in Computer Vision.

Chapter 2, An Introduction to the Basics of OpenCV, discusses how to read/write images and videos in OpenCV, and also explains how to build a project using CMake.

Chapter 3, Learning the Graphical User Interface and Basic Filtering, covers how to build a graphical user interface and mouse event detector to build interactive applications.

Chapter 4, Delving into Histograms and Filters, explores histograms and filters and also shows how we can cartoonize an image.

Chapter 5, Automated Optical Inspection, Object Segmentation, and Detection, describes various image preprocessing techniques, such as noise removal, thresholding, and contour analysis.

Chapter 6, Learning Object Classification, deals with object recognition and machine learning, and how to use Support Vector Machines to build an object classification system.

Chapter 7, Detecting Face Parts and Overlaying Masks, discusses face detection and Haar Cascades, and then explains how these methods can be used to detect various parts of the human face.

Chapter 8, Video Surveillance, Background Modeling, and Morphological Operations, explores background subtraction, video surveillance, and morphological image processing and describes how they are connected to each other.

Chapter 9, Learning Object Tracking, covers how to track objects in a live video using different techniques, such as color-based and feature-based tracking.

Chapter 10, Developing Segmentation Algorithms for Text Recognition, covers optical character recognition, text segmentation, and provides an introduction to the Tesseract OCR engine.

Chapter 11, Text Recognition with Tesseract, delves deeper into the Tesseract OCR Engine to explain how it can be used for text detection, extraction, and recognition.