PDF Ebook Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library
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Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library
PDF Ebook Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library
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Book Description
Software That Sees
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About the Author
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.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.
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Product details
Paperback: 1024 pages
Publisher: O'Reilly Media; 1 edition (January 8, 2017)
Language: English
ISBN-10: 1491937998
ISBN-13: 978-1491937990
Product Dimensions:
7 x 2.5 x 9.5 inches
Shipping Weight: 3.5 pounds (View shipping rates and policies)
Average Customer Review:
3.5 out of 5 stars
15 customer reviews
Amazon Best Sellers Rank:
#374,680 in Books (See Top 100 in Books)
With this book, I was able to write code to calibrate a pair of stereo webcams, undistort and rectify the images, perform stereo block matching, produce a disparity map, and turn that into a 3D point cloud (I used Point Cloud Library to actually visualize the points). Not too shabby.I have read only a fraction of the book so far. I just jumped in, with the explanation of camera calibration, and went from there.The book does a fairly good job explaining a classic computer vision pipeline, which OpenCV functions are useful at each stage, what the different parameters do, and provides a little bit of primer / math for those want to know "how it works." The book could be considered equal parts cookbook and "introduction to computer vision," with a healthy dose of commentary and foot-notes from the authors (it's not a terse book).The book is a good starting point, and good for rapid prototyping. You WILL need to read papers, online tutorials, and sample code to make use of OpenCV. But the book is better than the online docs, and well worth the price.
I love this book. There may be a couple of formatting issues here and there, but that is easy to overlook. The explanations are crystal clear and cover the background knowledge as well as the details of the opencv library. There are also references to the literature, and explanations of the choices made for the opencv implementations of algorithms. It's like having the original authors available for questioning when needed!
The books looks good, but I was unable to get the suggested environment to load on either my Mac Pro or on a Win10 machine. So as a result I have put the book aside until someone generates an installation program that works for the suggested environment on a common machine. The book's directions are either outdated or incomplete, I have no idea which...I'm not a hard core Computer Vision expert, I was just curious about how the field is progressing and wanted to get some experience, but based on the above, I have gone on to other interests..
This is a good reference book for OpenCV 3. I'd like to see more examples and colorful pages instead of just black and white because we don't only process grayscale images. OpenCV 4 version of it should be published for the newest release of OpenCV.
It has proven to be an excellent resource to get me caught up on your standards for computer vision and programming for it
The book is great, easy to understand, good examples, etc.; I seriously don't understand the bad reviews.
Terribly printed. Code is just thrown in plain text without any distinction. Super lazy. I don't know why we have to make a book that is mostly printed code these days... just make a github repo.
Finally got it after waiting several years. Great book about the latest OpenCV 3
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