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Learning Opencv: Computer Vision with the Opencv Library

   
지은이 Gary Bradski   |   출판사 O'REILLY  |   발행일 2008년 10월 04일   |   언어 : English
 
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판매가 69,990원57,390원 18%
마일리지 1% 700원
발행일 2008-10-04 | 중량: 0.91 kg | 사이즈: 19.2*22.1*3 cm
ISBN 0596516134 |  9780596516130
기타정보 원서 | 0쪽 | $ 49.99 | Paperback
예상입고일 7~12일 소요예정 (근무일기준)
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컴퓨터비전
종합지수 32p 2 위
   
이 책의 번역서
  OpenCV 제대로 배우기
한빛미디어 | Gary Bradski
주의사항
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  • - 해외주문원서는 취소 및 반품이 불가능합니다.(단, 파본이나 오발송은 제외 입니다.)
  • - 수입절차/입고소요기간 자세히
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    Product Description

    Learning OpenCV puts you right in the middle of the rapidly expanding field of computer vision. Written by the creators of OpenCV, the widely used free open-source 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 the data.

    Computer vision is everywhere -- in security systems, manufacturing inspection systems, medical image analysis, Unmanned Aerial Vehicles, and more. It helps robot cars drive by themselves, stitches Google maps and Google Earth together, checks the pixels on your laptop's LCD screen, and makes sure the stitches in your shirt are OK.

    OpenCV provides an easy-to-use computer vision infrastructure along with a comprehensive library containing more than 500 functions that can run vision code in real time. With Learning OpenCV, any developer or hobbyist can get up and running with the framework quickly, whether it's to build simple or sophisticated vision applications.

    The book includes:

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

    Hands-on exercises at the end of each chapter help you absorb the concepts, and an appendix explains how to set up an OpenCV project in Visual Studio. OpenCV is written in performance optimized C/C++ code, runs on Windows, Linux, and Mac OS X, and is free for commercial and research use under a BSD license.

    Getting machines to see is a challenging but entertaining goal. If you're intrigued by the possibilities, Learning OpenCV gets you started on building computer vision applications of your own.
  • 준비 중입니다.
    About the Author

    Dr. Gary Rost Bradski is VP of Technology at Rexee Inc. a new startup applying machine learning to rich media on the web. He is also a consulting professor in the CS department at Stanford University, AI Lab where he mentors robotics, machine learning and computer vision research. He has a BS degree in EECS from U.C. Berkeley and a PhD from Boston University. His current interest is in applying highly scalable statistical models in computer vision and in continuous machine "learning in clutter" in robotics in general. Some external tools he started for this are 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 (for example in wide use within Google). All libraries are open, and free on Source Forge for commercial or research purposes. The vision libraries use and helped develop a notable part of the commercial Intel performance primitives library (IPP). Gary led the vision team for Stanley, the Stanford robot that won the DARPA Grand Challenge autonomous race across the desert for a $2M team prize. 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, and computer vision. 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.
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