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Naum Lukin
Naum Lukin

Learning OpenCV 3: Computer Vision In C With ...


OpenCV (Open Source Computer Vision Library) is a library of programming functions mainly for real-time computer vision.[1] Originally developed by Intel, it was later supported by Willow Garage, then Itseez (which was later acquired by Intel[2]). The library is cross-platform and licensed as free and open-source software under Apache License 2. Starting in 2011, OpenCV features GPU acceleration for real-time operations.[3]




Learning OpenCV 3: Computer vision in C with ...



"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:


Besides Python, I mainly use C, C++, Objective-C, Java, and C#. I also used Ruby and ActionScript 3 a lot in my student days, the latter being the language of my thesis project, an augmented reality framework. For me, the choice of platform and libraries tends to come before the choice of language in any given project. Much of my current work is in computer vision for embedded systems and, here, I like to prototype in Python and then refine the implementation in C++. Above all, C++ never leaves me with regrets about performance or access to lower levels.


Then, OpenCV 3 came out and we found ourselves in the situation of patching and updating the existing books while also trying to expand the lineup. Packt brought on five great co-authors who made this work possible. Joe Minichino updated and greatly expanded the Python OpenCV book for its second edition, Learning OpenCV 3 Computer Vision with Python. He made it strong as both a machine learning book and a computer vision book for all levels of readers. Alejandro Rodas de Paz wrote Python Game Development by Example, to which I contributed a computer vision chapter. This book offers a fun and accessible set of game projects using a broad selection of Python libraries. And I teamed up with Steven Puttemans, Quan Hua, and Utkarsh Sinha to write our most advanced book, OpenCV 3 Blueprints, based on some of the best techniques that we had tested in research and industry. For this, I wrote about topics I love: cameras, lenses, and computational photography.


Combined, the books' English editions alone have sold more than 28,000copies, representing a goodly crowd of computer vision students andprofessionals. If you are already a reader, thank you! If not, please consider becomingone. Together, we are learning to see the world through a new set of lenses.


Bear in mind that my books focus on computer vision, applicationdevelopment, and game development, rather than programming languages perse. If you are unfamiliar with a language, you may want to find ageneral-purpose guide to the language, too.


Take a quick glance at Lines 7-10. These lines allow us to provide additional information to our program at runtime from within the terminal. Command line arguments are used heavily on the PyImageSearch blog and in all other computer science fields as well.


Do you think learning computer vision and deep learning has to be time-consuming, overwhelming, and complicated? Or has to involve complex mathematics and equations? Or requires a degree in computer science?


Tesseract acquired maturity with version 3.x when it started supporting many image formats and gradually added many scripts (languages). Tesseract 3.x is based on traditional computer vision algorithms. In the past few years, Deep Learning based methods have surpassed traditional machine learning techniques by a huge margin in terms of accuracy in many areas of Computer Vision. Handwriting recognition is one of the prominent examples. So, it was just a matter of time before Tesseract too had a Deep Learning based recognition engine.


In 2007, right after finishing my Ph.D., I co-founded TAAZ Inc. with my advisor Dr. David Kriegman and Kevin Barnes. The scalability, and robustness of our computer vision and machine learning algorithms have been put to rigorous test by more than 100M users who have tried our products.


Joe Minichino is a computer vision engineer for Hoolux Medical by day and a developer of the NoSQL database LokiJS by night. On weekends, he is a heavy metal singer/songwriter. He is a passionate programmer who is immensely curious about programming languages and technologies and constantly experiments with them. At Hoolux, Joe leads the development of an Android computer vision-based advertising platform for the medical industry.


Nandan Banerjee has a bachelor's degree in computer science and a master's in robotics engineering. He started working with Samsung Electronics right after graduation. He worked for a year at its R&D centre in Bangalore. He also worked in the WPI-CMU team on the Boston Dynamics' robot, Atlas, for the DARPA Robotics Challenge. He is currently working as a robotics software engineer in the technology organization at iRobot Corporation. He is an embedded systems and robotics enthusiast with an inclination toward computer vision and motion planning. He has experience in various languages, including C, C++, Python, Java, and Delphi. He also has a substantial experience in working with ROS, OpenRAVE, OpenCV, PCL, OpenGL, CUDA and the Android SDK.


Tian Cao is pursuing his PhD in computer science at the University of North Carolina in Chapel Hill, USA, and working on projects related to image analysis, computer vision, and machine learning.


Haojian Jin is a software engineer/researcher at Yahoo! Labs, Sunnyvale, CA. He looks primarily at building new systems of what's possible on commodity mobile devices (or with minimum hardware changes). To create things that don't exist today, he spends large chunks of his time playing with signal processing, computer vision, machine learning, and natural language processing and using them in interesting ways. You can find more about him at -3.com/


Adrian Rosebrock is an author and blogger at He holds a PhD in computer science from the University of Maryland, Baltimore County, USA, with a focus on computer vision and machine learning.


He has consulted for the National Cancer Institute to develop methods that automatically predict breast cancer risk factors using breast histology images. He has also authored a book, Practical Python and OpenCV ( ), on the utilization of Python and OpenCV to build real-world computer vision applications.


OpenCV 3 is a state-of-the-art computer vision library that is used for a variety of image and video processing operations. Some of the more spectacular and futuristic features, such as face recognition or object tracking, are easily achievable with OpenCV 3. Learning the basic concepts behind computer vision algorithms, models, and OpenCV's API will enable the development of all sorts of real-world applications, including security and surveillance tools.


Starting with basic image processing operations, this book will take you through a journey that explores advanced computer vision concepts. Computer vision is a rapidly evolving science whose applications in the real world are exploding, so this book will appeal to computer vision novices as well as experts of the subject who want to learn about the brand new OpenCV 3.0.0.


This book is aimed at programmers with working knowledge of Python as well as people who want to explore the topic of computer vision using the OpenCV library. No previous experience of computer vision or OpenCV is required. Programming experience is recommended.


Training. For training, we used the binary cross-entropy loss function [21] with respect to the gold standard labels made by our expert panels. We then trained our classification model using two learning strategies of 1) a standard stochastic gradient descent algorithm [25] and 2) AdaDelta [26] with an adaptive learning rate. Furthermore, we tested our model with the varying number of convolutional filters K set to 8, 16, 32, or 64. The best strategy with the maximum performance was determined by using a second hold-out test set which was prepared by randomly selecting 10% of the training set, described in Data section, to be used for computing performance statistics of a model trained with the remaining 90% in order to minimize over-fitting.


For a hands-on: Learning OpenCV: Computer Vision with the OpenCV Library by Gary Bradski and Adrian Kaehler. This book will give you a nice introduction to a lot of CV topics, references to go further and code examples using OpenCV, probably the most used computer vision library nowadays (recently, NVidia announced porting part of the code to their GPUs). OpenCV presents C, C++ and Python APIs. 041b061a72


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