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OpenCV Computer Vision Application Programming Cookbook Second Edition

OpenCV Computer Vision Application Programming Cookbook Second Edition

By : Robert Laganiere
3.7 (3)
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OpenCV Computer Vision Application Programming Cookbook Second Edition

OpenCV Computer Vision Application Programming Cookbook Second Edition

3.7 (3)
By: Robert Laganiere

Overview of this book

OpenCV 3 Computer Vision Application Programming Cookbook is appropriate for novice C++ programmers who want to learn how to use the OpenCV library to build computer vision applications. It is also suitable for professional software developers wishing to be introduced to the concepts of computer vision programming. It can also be used as a companion book in a university-level computer vision courses. It constitutes an excellent reference for graduate students and researchers in image processing and computer vision.
Table of Contents (13 chapters)
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12
Index

What this book covers

Chapter 1, Playing with Images, introduces the OpenCV library and shows you how to build simple applications that can read and display images. It also introduces the basic OpenCV data structures.

Chapter 2, Manipulating Pixels, explains how an image can be read. It describes different methods for scanning an image in order to perform an operation on each of its pixels.

Chapter 3, Processing Color Images with Classes, consists of recipes that present various object-oriented design patterns that can help you build better computer vision applications. It also discusses the concept of colors in images.

Chapter 4, Counting the Pixels with Histograms, shows you how to compute image histograms and how they can be used to modify an image. Different applications based on histograms are presented, and they achieve image segmentation, object detection, and image retrieval.

Chapter 5, Transforming Images with Morphological Operations, explores the concept of mathematical morphology. It presents different operators and informs you how they can be used to detect edges, corners, and segments in images.

Chapter 6, Filtering the Images, teaches you the principle of frequency analysis and image filtering. It shows how low-pass and high-pass filters can be applied to images and presents the concept of derivative operators.

Chapter 7, Extracting Lines, Contours, and Components, focuses on the detection of geometric image features. It explains how to extract contours, lines, and connected components in an image.

Chapter 8, Detecting Interest Points, describes various feature-point detectors in images.

Chapter 9, Describing and Matching Interest Points, explains how descriptors of interest points can be computed and used to match points between images.

Chapter 10, Estimating Projective Relations in Images, explores the projective relations that exist between two images of the same scene. It also describes the process of camera calibration and revisits the problem of matching feature points.

Chapter 11, Processing Video Sequences, provides you with a framework to read and write a video sequence and process its frames. It also shows you how it is possible to track feature points from frame to frame and how to extract the foreground objects moving in front of a camera.

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