Computer vision is a field dealing with developing methods for the automatic acquisition, processing, analyzing, and understanding of images and videos by computers, in order to produce decisions that help humans in their daily tasks. This book provides an overview and description of the basic concepts of image and video processing. It also includes explanations of how to use the relevant OpenCV library routines; full working programs are available online (the website is provided in the book).

Chapter 1 provides a very brief introduction to the extraordinarily difficult problem that computer vision systems try to solve, that is, to duplicate the human vision system. It includes some applications of computer vision, such as the automatic reading of license plates, biometric security checks at airports, advanced interfaces for computer games, and so on. The next few chapters take the reader through the computer vision concepts.

Chapter 2 describes the basics of cameras and images, along with techniques for removing or reducing noise. In chapter 3, the author examines how images can be processed into histograms (a vector containing the frequency of each color in the image), which is the type of information that can be used by computers. Chapter 4 introduces a simplified form of computer vision, binary vision, in which each image is transformed into a black-and-white image.

In chapter 5, the author discusses techniques to remove distortion introduced by camera lens from images. Chapters 6 and 7 deal with techniques used for edge detection (locations at which the brightness or color changes significantly) in images, and corner (the intersection of two edges) and feature detection. Chapter 8 introduces techniques for object recognition, such as template matching, chamfer matching, statistical pattern recognition, Haar classifiers, and support vector machines (SVMs). It concludes with an introduction to the performance assessment methodology.

Chapter 9 looks at the basics of video processing involving two tasks: detecting and tracking moving objects within a video. Chapter 10 presents 20 application problems; the solutions are provided online. The basic concepts are presented in a straightforward way; each includes an example and demonstrating figures, along with a snippet of OpenCV code. This way readers can immediately implement the concept into their applications. It would have been better if the chapters had contained references.

The title of the book advertises it as “a practical introduction to computer vision [using] OpenCV,” which is exactly what this book is. It provides a solid background to basic computer vision concepts. Each concept is presented along with its practical part (that is, code), which actually is the part the reader will use in real applications and is the reason she/he would read a computer vision book. This way the book “bridge[s] the gap between the theory and the practical implementation of computer vision.”

Although there are many computer vision books on the market that offer a more comprehensive approach to explaining the computer vision concepts, extremely few offer such comprehensive practical examples. In this context, the book would be very welcome by beginner code developers. This is not a book for an advanced computer vision developer or someone trying to understand the field in deep detail. On the other hand, if the reader is looking for a book containing a set of tutorials to get her/him started on creating and using OpenCV code to solve an application, this is a great choice.