Acquire, process, and analyze visual content to build full-fledged imaging applications using OpenCVAbout 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
In Detail
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.
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.
Mª del Milagro Fernandez Carrobles received her Bachelor's degree in Computer Science and Master's degree in Physics and Mathematics from the University of Castilla-La Mancha, Spain, in 2010 and 2011, respectively. She is currently a PhD candidate and works at Visilabs. Her research interests include image processing and artificial intelligence, especially in medical imaging.
Noelia Vallez Enano liked computers since childhood, although she didn't have one before her mid-teens. In 2009, she finished her studies in Computer Science through the University of Castilla-La Mancha. She started her work at the Visilabs group through a project about mammography CAD systems and electronic health records. Since then, she has obtained a Master's degree in Physics and Mathematics and has enrolled for a PhD degree. Her work involves using image processing and pattern recognition methods. She also likes to teach and work in other areas of artificial intelligence.
Gloria Bueno Garcia holds a PhD in Machine Vision from Coventry University, UK. She has experience working as a principal researcher in several research centers, such as UMR 7005 research unit CNRS, Louis Pasteur University, Strasbourg, France; Gilbert Gi
