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Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library

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Get started in the rapidly expanding field of computer vision with this practical guide. Written by Adrian Kaehler and Gary Bradski, creator of the open source OpenCV library, this book provides a thorough introduction for developers, academics, roboticists, and hobbyists. You'll learn what it takes to build applications that enable computers to "see" and make decisions ba Get started in the rapidly expanding field of computer vision with this practical guide. Written by Adrian Kaehler and Gary Bradski, creator of the open source OpenCV library, this book provides a thorough introduction for developers, academics, roboticists, and hobbyists. You'll learn what it takes to build applications that enable computers to "see" and make decisions based on that data. With over 500 functions that span many areas in vision, OpenCV is used for commercial applications such as security, medical imaging, pattern and face recognition, robotics, and factory product inspection. This book gives you a firm grounding in computer vision and OpenCV for building simple or sophisticated vision applications. Hands-on exercises in each chapter help you apply what you've learned. This volume covers the entire library, in its modern C++ implementation, including machine learning tools for computer vision. Learn OpenCV data types, array types, and array operations Capture and store still and video images with HighGUI Transform images to stretch, shrink, warp, remap, and repair Explore pattern recognition, including face detection Track objects and motion through the visual field Reconstruct 3D images from stereo vision Discover basic and advanced machine learning techniques in OpenCV


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Get started in the rapidly expanding field of computer vision with this practical guide. Written by Adrian Kaehler and Gary Bradski, creator of the open source OpenCV library, this book provides a thorough introduction for developers, academics, roboticists, and hobbyists. You'll learn what it takes to build applications that enable computers to "see" and make decisions ba Get started in the rapidly expanding field of computer vision with this practical guide. Written by Adrian Kaehler and Gary Bradski, creator of the open source OpenCV library, this book provides a thorough introduction for developers, academics, roboticists, and hobbyists. You'll learn what it takes to build applications that enable computers to "see" and make decisions based on that data. With over 500 functions that span many areas in vision, OpenCV is used for commercial applications such as security, medical imaging, pattern and face recognition, robotics, and factory product inspection. This book gives you a firm grounding in computer vision and OpenCV for building simple or sophisticated vision applications. Hands-on exercises in each chapter help you apply what you've learned. This volume covers the entire library, in its modern C++ implementation, including machine learning tools for computer vision. Learn OpenCV data types, array types, and array operations Capture and store still and video images with HighGUI Transform images to stretch, shrink, warp, remap, and repair Explore pattern recognition, including face detection Track objects and motion through the visual field Reconstruct 3D images from stereo vision Discover basic and advanced machine learning techniques in OpenCV

49 review for Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library

  1. 5 out of 5

    Robert

    The reason I chose this book is because it seemed it has an in-depth explanation of the many features of the OpenCV library, and I was right. So in-depth in fact, that it doesn't just explain what individual functions or classes do, but in many cases the theoretical background of the underlying mechanisms is also explained in detail. So much detail, that sometimes it feels like it is an academic research paper and not a book trying to introduce the reader to the concepts in a usable way. To be h The reason I chose this book is because it seemed it has an in-depth explanation of the many features of the OpenCV library, and I was right. So in-depth in fact, that it doesn't just explain what individual functions or classes do, but in many cases the theoretical background of the underlying mechanisms is also explained in detail. So much detail, that sometimes it feels like it is an academic research paper and not a book trying to introduce the reader to the concepts in a usable way. To be honest, these explanations get tiresome and hard to comprehend occasionally, so much, that I found myself looking up other resources to understand what is exactly going on. Make no mistake, OpenCV is not a toy library, and perhaps it really does deserve such deep explanations, but I'm not sure if the amount of that was chosen correctly. This becomes evident when considering, that despite the fact that the text tries really hard to help comprehend certain topics and how different components work together, sometimes it is still far from being evident how and what should be used for certain tasks. The included code examples and code walkthroughs help in this regard a lot, but sometimes even after getting through those, the reader might remain puzzled. As mentioned before, reading additional online resources seems to be a necessity with this book, which is surprising considering the length of it (about a thousand pages). As mentioned above, the book has a lot of explanations, and fortunately enough they are organized into different subjects of computer vision, like filters, image transforms, histograms, keypoints and descriptors, tracking, background subtraction, camera models and so on. Obviously, without a firm understanding of the library structure, basic data types and operations it would be impossible to write any sort of CV application, thus the book dedicates about a quarter of its entirety to these subjects as well. It is quite comprehensive in this regard, as Chapter 5 alone is nothing but detailed explanations of most available array operations, but this sort of approach can get really boring and tiresome (again), so should you pick this book up, expect a long and very bumpy ride. Overall, this book is very comprehensive, gives a ton of details about just about any topics it touches, but I still couldn't stop feeling that there is something lacking. Despite the fact that there is no shortage of explanations and examples, and there are even some pretty good exercises at the end of chapters, the book still feels like it is just a very detailed library documentation and doesn't add too much of its own to the whole topic, or in other words, there was no sense of guidance at all. Don't get me wrong, it is still worth picking this up, but perhaps this shouldn't be the first piece one reads on the subject. In my opinion it is better suited to be used as a reference book that is read in parallel with another one when some questions arise. Because of these, I just can't recommend it to be read without at least having something else on the subject as well, hence the 3 stars.

  2. 4 out of 5

    Itai

    This book is extensive, it tries to cover (almost) the entirety of the OpenCV library. This is a tremendous undertaking, and while it brings clarity to some never changing fundamentals of image processing (such as extracting features, camera calibration, etc.) it spends too long on areas that have been replaced by newer technologies and algorithms. Specifically in the areas of GUI and machine learning. This is not to say that the book is not an achievement, it is, but read it with a critical eye This book is extensive, it tries to cover (almost) the entirety of the OpenCV library. This is a tremendous undertaking, and while it brings clarity to some never changing fundamentals of image processing (such as extracting features, camera calibration, etc.) it spends too long on areas that have been replaced by newer technologies and algorithms. Specifically in the areas of GUI and machine learning. This is not to say that the book is not an achievement, it is, but read it with a critical eye, and know when to skip certain parts. Overall, I would recommend using this book as a reference, pick it up when you need a refresher on certain OpenCV elements and principals, especially where the current official documentation lacks (which is unfortunately the rule rather than the exception). For teachers, tutors, and those who wish to challenge themselves I would particularly recommend looking at the questions/challenges presented at the end of each chapter, either as inspiration for tests, assignments for courses, or mini-projects to familiarize oneself with OpenCV.

  3. 5 out of 5

    Vô Danh

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  4. 4 out of 5

    Ante Javor

    Great book for someone with good understanding of computer vision looking for overview of OpenCV.

  5. 5 out of 5

    Aigyuh

  6. 4 out of 5

    Matthew Sowders

  7. 4 out of 5

    Kevin

  8. 4 out of 5

    امیر راشدی

  9. 5 out of 5

    Xinhua Wen

    Very wordy very clumsy too describe some logical idea that is rather simple.Yet when it come to key algorithm became vague and ambiguous.You can hardly find any page that is error free most of its content is just a cheap copy and paste from online documentation that is already there.Extremely dreadful to read for a beginner not much use for an expert.

  10. 5 out of 5

    Williams L

  11. 4 out of 5

    Amarjyoti

  12. 5 out of 5

    Soso

  13. 5 out of 5

    Omar Sleam

  14. 5 out of 5

    Sören von Sebelin

  15. 5 out of 5

    Narinder

  16. 4 out of 5

    Anya

  17. 4 out of 5

    Fabian

  18. 4 out of 5

    shane

  19. 4 out of 5

    Zhen Lyu

  20. 5 out of 5

    Khanh Chuong

  21. 5 out of 5

    Giorgio Natili

  22. 5 out of 5

    Christina Bi

  23. 5 out of 5

    hai

  24. 4 out of 5

    Shubham Verma

  25. 4 out of 5

    Brandon

  26. 5 out of 5

    Alexander

  27. 4 out of 5

    Izzo

  28. 5 out of 5

    Paul

  29. 4 out of 5

    Seyed Ali

  30. 5 out of 5

    Abbas

  31. 5 out of 5

    Rayuh

  32. 4 out of 5

    Udaya Wijenayake

  33. 5 out of 5

    Pj Texier

  34. 4 out of 5

    Raja Rathinam

  35. 5 out of 5

    Brian

  36. 4 out of 5

    Elena Godyaykina

  37. 5 out of 5

    Lee Dong

  38. 4 out of 5

    Amit

  39. 5 out of 5

    Tian Zhi

  40. 4 out of 5

    Alvaro Dasso

  41. 5 out of 5

    Doron Amir

  42. 5 out of 5

    Nallu Harshavardhan

  43. 5 out of 5

    Prabhat

  44. 5 out of 5

    Anukriti Bansal

  45. 4 out of 5

    Timmy Ghiurău

  46. 4 out of 5

    Serkan Safi

  47. 5 out of 5

    Duc Tran tien

  48. 5 out of 5

    Iliyan Gochev

  49. 4 out of 5

    冠誼 李

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