Learning OpenCV: Computer Vision with the OpenCV Library

Gary Bradski and Adrian Kaehler

ISBN 10: 0596516134 / ISBN 13: 9780596516130

Edité par OReilly Media, 2008

Dispatched, from the UK, within 48 hours of ordering. Though second-hand, the book is still in very good shape. Minimal signs of usage may include very minor creasing on the cover or on the spine. Damaged cover. The cover of is slightly damaged for instance a torn or bent corner. N° de réf. du vendeur CHL10392801

Synopsis :

"This library is useful for practitioners, and is an excellent tool for those entering the field: it is a set of computer vision algorithms that work as advertised." -William T. Freeman, Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology

Learning OpenCV puts you in the middle of the rapidly expanding field of computer vision. Written by the creators of the free open source OpenCV library, this book introduces you to computer vision and demonstrates how you can quickly build applications that enable computers to "see" and make decisions based on that data.

Computer vision is everywhere-in security systems, manufacturing inspection systems, medical image analysis, Unmanned Aerial Vehicles, and more. It stitches Google maps and Google Earth together, checks the pixels on LCD screens, and makes sure the stitches in your shirt are sewn properly. OpenCV provides an easy-to-use computer vision framework and a comprehensive library with more than 500 functions that can run vision code in real time.

Learning OpenCV will teach any developer or hobbyist to use the framework quickly with the help of hands-on exercises in each chapter. This book includes:

• A thorough introduction to OpenCV
• Getting input from cameras
• Transforming images
• Segmenting images and shape matching
• Pattern recognition, including face detection
• Tracking and motion in 2 and 3 dimensions
• 3D reconstruction from stereo vision
• Machine learning algorithms

Getting machines to see is a challenging but entertaining goal. Whether you want to build simple or sophisticated vision applications, Learning OpenCV is the book you need to get started.

À propos des auteurs: Dr. Gary Rost Bradski is a consulting professor in the CS department at Stanford University AI Lab where he mentors robotics, machine learning and computer vision research. He is also Senior Scientist at Willow Garage http: //www.willowgarage.com, a recently founded robotics research institute/incubator. He has a BS degree in EECS from U.C. Berkeley and a PhD from Boston University. He has 20 years of industrial experience applying machine learning and computer vision spanning option trading operations at First Union National Bank, to computer vision at Intel Research to machine learning in Intel Manufacturing and several startup companies in between. Gary started the Open Source Computer Vision Library (OpenCV http: //sourceforge.net/projects/opencvlibrary/ ), the statistical Machine Learning Library (MLL comes with OpenCV), and the Probabilistic Network Library (PNL). OpenCV is used around the world in research, government and commercially. The vision libraries helped develop a notable part of the commercial Intel performance primitives library (IPP http: //tinyurl.com/36ua5s). Gary also organized the vision team for Stanley, the Stanford robot that won the DARPA Grand Challenge autonomous race across the desert for a $2M team prize and helped found the Stanford AI Robotics project at Stanford http: //www.cs.stanford.edu/group/stair/ working with Professor Andrew Ng. Gary has over 50 publications and 13 issued patents with 18 pending. He lives in Palo Alto with his wife and 3 daughters and bikes road or mountains as much as he can.

Dr. Adrian Kaehler is a senior scientist at Applied Minds Corporation. His current research includes topics in machine learning, statistical modeling, computer vision and robotics. Adrian received his Ph.D. in Theoretical Physics from Columbia university in 1998. Adrian has since held positions at Intel Corporation and the Stanford University AI Lab, and was a member of the winning Stanley race team in the DARPA Grand Challenge. He has a variety of published papers and patents in physics, electrical engineering, computer science, and robotics.

Détails bibliographiques

Titre : Learning OpenCV: Computer Vision with the ...
Éditeur : OReilly Media
Date d'édition : 2008
Reliure : Paperback
Etat : Used; Very Good