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OpenCV Essentials (Anglais) Broché – 25 août 2014
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Description du produit
Présentation de l'éditeur
About This Book
- Create OpenCV programs with a rich user interface
- Develop real-world imaging applications using free tools and libraries
- Understand the intricate details of OpenCV and its implementation using easy-to-follow examples
Who This Book Is For
This book is intended for C++ developers who want to learn how to implement the main techniques of OpenCV and get started with it quickly. Working experience with computer vision / image processing is expected.
What You Will Learn
- Explore advanced image processing techniques such as the retina algorithm, morphing, and color transfer
- Create programs using advanced segmentation tools such as the new connectedComponents and connectedComponentsWithStats functions
- Use flood filling along with the watershed transform to obtain better segmentations
- Explore the new powerful KAZE features
- Use advanced video-based background/foreground segmentation for class BackgroundSubtractor and ECC-based warping
- Leverage the available object detection frameworks and the new scene text detection functionality
- Get a grasp of advanced topics such as machine learning and GPU optimization
OpenCV, arguably the most widely used computer vision library, includes hundreds of ready-to-use imaging and vision functions used in both academia and industry. It mainly focuses on real-time image processing. As cameras get cheaper and imaging features grow in demand, the range of applications using OpenCV increases significantly, both for desktop and mobile platforms.
The book provides an example-based tour of OpenCV's main modules and algorithms, including the latest available in version 3.0. Starting with the setup and description of the library, this book teaches you how to add graphical user interface capabilities to OpenCV programs. Further, you will learn about the essential techniques such as image processing, image segmentation, object detection, and motion, which will help you process and analyze images better. You will also learn how to extract 2D features from images and how to take advantage of machine learning. By the end of this book, you will completely understand how to put those computer vision techniques into practice.
Biographie de l'auteur
Oscar Deniz Suarez
Oscar Deniz Suarez is the author of more than 50 refereed papers in journals and conferences. His research interests are mainly focused on computer vision and pattern recognition. He received the runner-up award for the best PhD work on computer vision and pattern recognition by AERFAI and the Image File and Reformatting Software challenge award by InnoCentive Inc. He has been a national finalist at the 2009 Cor Baayen awards. His work is being used by cutting-edge companies such as Existor, GLIIF, TapMedia, E-Twenty, and others and has also been added to OpenCV. Currently, he works as an associate professor at the University of Castilla-La Mancha and contributes to Visilabs. He is a senior member of IEEE and is affiliated with AAAI, SIANI, CEA-IFAC, AEPIA, AERFAI-IAPR, and The Computer Vision Foundation. He serves as an academic editor for the journal PLOS ONE. He has been a visiting researcher at Carnegie Mellon University, Pennsylvania, USA; Imperial College, London, UK; and Leica Biosystems, Ireland. He has co-authored a book on OpenCV programming for mobile devices.
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La bibliothèque OpenCV, disponible en accès libre, permet d'utiliser de nombreuses options de vision automatique, et ce livre est très utile pour s'y retrouver rapidement et efficacement.
Ce livre permet de rentrer dans l'utilisation pratique de OpenCV sans donner un cours de traitement d'image, mais bon, on a presque tout vu beaucoup des concepts si on a manipulé des éditeurs d'images.
Il recommende une installation qui exploite Qt- ca tombe bien ca m'arrange.
On passe très vite à des petits projets qui font intervenir les concepts essentiels, mais vous n'aurez pas un manuel de référence.
On attend un livre plus complet, mais pour le prix dérisoire c'est un cadeau!
Commentaires client les plus utiles sur Amazon.com
The book is based on OpenCV 2.x and does not cover the changes introduced in OpenCV 3.0 (although some references are made in advance to certain methods which were introduced in version 3.0). Some searching through the OpenCV docs or googling is therefore required to get the code examples working in OpenCV 3.
All in all, this is a good introduction to OpenCV. For a more detailed presentation of computer vision and OpenCV, I am eagerly waiting for Learning OpenCV 3 (o'Reilly Publishing) due to be published in August 2016. Unfortunately, the publishing date has been pushed forward at least a couple of times now. Based on a previous edition of Learning OpenCV, it should be very detailed and rather comprehensive but also more math and theory heavy. The edition I have is based on OpenCV 1.0 so it is now useful mostly for learning about the methodology of computer vision.
Besides a more detailed look on the various methods of computer vision, the reader may find necessary a book on machine learning. This is a very vast topic in itself. Personnally, I recently bought the book Python Machine Learning by Sebastian Raschka. At first glance, it seems to complement OpenCV Essentials nicely, going into much more detail on the methodology of machine learning.
This book is for people who have at least basic knowledge in OpenCV and Computer Vision, if you are new in OpenCV maybe this book won’t be the suitable one. Additionally all the examples are in C++ for Visual Studio, I did not like the last restriction, but it was necessary for some examples. This book show some upcoming OpenCV functions for the next release 3.0 (available at this time in Alpha version), and there are a lot of very useful functions.
Let’s begin with the main review. First chapters are about OpenCV basic functionalities (load images, brightness, contrast, color spaces, arithmetic and geometrical transforms). For every topic they show the code, explanation and results. The book shows tips in each topic, giving you the opportunity to investigate more about the functionality, and also they usually share links to Scientific Papers, thus, if you have a better idea to improve that function it helps you to do research about it.
I found Threshold topic in Chapter 4 (What’s in the Image? Segmentation) very useful and didactic, a lot of developers (also experts) made mistakes in this topic when choosing the type of threshold. Until here, Chapters were about main OpenCV functions, but here on-wards the book becomes better and very useful with content about Pattern Recognition, Classification, also Video Processing. Thus, I will discuss separated every chapter.
Chapter 5: Focusing on the Interesting 2D Features: This Chapter is totally based in Keypoints and its variations, the book provide a lot of examples and also the matching between images using Keypoints. Additionally some new OpenCV feature detectors of the new release are explained (KAZE and AKAZE).
Chapter 6: Where’s Wally? Object Detection: The main topics are Cascade detectors and Latent SVM. The book shows an example of basic pedestrian detection in a few lines of code using HOG cascade detector. Also, if you need to train your own cascade, you will find a detailed explanation about how to do it.
Chapter 7: What is he Doing? Motion: Due to I developed this kind of systems, I particularly liked this Chapter. Topics like Video tracking, Motion and Background subtraction are now included in the new OpenCV 3.0 and certainly will be very useful for everyone who is developing/researching this area. The book does not show the typical Optical Flow, but explains and optimized and faster Lucas-Kanade optical flow instead.
Chapter 8: Advanced Topics: If you are interested in Machine learning, Classification, or CUDA GPU programming this Chapter will be very useful too. It shows how to use the Random Forest classifier, one of the best classifiers available in recognition power and efficiency. Also the typical SVM is explained with a simple version of a recognition system.
To summarize, if you are using OpenCV for basic image processing certainly you will be more interested in Chapter 1 to 4, and maybe 5. If you need more depth knowledge and want to do complex Pattern Recognition systems you will be interested in Chapter 5 on-wards. The main weak point of this book is about pre-processing image explanation, although authors show some of them, they do not explain the real potential of the pre-processing step for this kind of systems. In all my experience I used a lot of pre-processing functions to improve results. Simple functions like erode, dilate and smoothing to avoid noise in images are very useful in all Computer Vision projects. Afterwards, I found this book very useful and recommendable.
Overall, this is a must-read book for programmers, researchers, and people eager with basic OpenCV, Computer Vision and C++ knowledge.