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 the Colors of an Image, consists of recipes presenting various object-oriented design patterns that can help you to 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 that 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 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 detector 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 in the same scene. It also describes how to detect specific targets in an image.

Chapter 11, Reconstructing 3D scenes, allows you to reconstruct the 3D elements of a scene from multiple images and recover the camera pose. It also includes a description of the camera calibration process.

Chapter 12, Processing Video Sequences, provide a framework to read and write a video sequence and to process its frames. It shows you also how it is possible to extract the foreground objects moving in front of a camera.

Chapter 13, Tracking Visual Motion, addresses the visual tracking problem. It shows you how to compute the apparent motion in videos. It also explains how to track moving objects in an image sequence.

Chapter 14, Learning from Examples, introduces basic concepts in machine learning. It shows how object classifiers can be built from image samples.

This cookbook is based on the C++ API of the OpenCV library. It is therefore assumed that you have some experience with the C++ language. In order to run the examples presented in the recipes and experiment with them, you need a good C++ development environment. Microsoft Visual Studio and Qt are two popular choices. 

This 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 who wants to be introduced to the concepts of computer vision programming. It can be used as a companion book in university-level computer vision courses. It constitutes an excellent reference for graduate students and researchers in image processing and computer vision.

In this book, you will find several headings that appear frequently (Getting ready, How to do it, How it works, There's more, and See also). To give clear instructions on how to complete a recipe, we use these sections as follows:

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "We can include other contexts through the use of the include directive."

A block of code is set as follows:

    // Compute Laplacian using LaplacianZC class
    LaplacianZC laplacian;
    laplacian.setAperture(7); // 7x7 laplacian
    cv::Mat flap= laplacian.computeLaplacian(image);
    laplace= laplacian.getLaplacianImage();

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

    // Compute Laplacian using LaplacianZC class
    LaplacianZC laplacian;
    laplacian.setAperture(7); // 7x7 laplacian
    cv::Mat flap= laplacian.computeLaplacian(image);
    laplace= laplacian.getLaplacianImage();

New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "Clicking the Next button moves you to the next screen."

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