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OpenCV Android Programming By Example. Leverage OpenCV to develop vision-aware and intelligent Android applications

Język publikacji: 1
Autorzy:
Amgad Muhammad, Erik Hellman, Erik A Westenius, Amgad M Ahmed Muhammad
OpenCV Android Programming By Example. Leverage OpenCV to develop vision-aware and intelligent Android applications Amgad Muhammad, Erik Hellman, Erik A Westenius, Amgad M Ahmed Muhammad - okladka książki

OpenCV Android Programming By Example. Leverage OpenCV to develop vision-aware and intelligent Android applications Amgad Muhammad, Erik Hellman, Erik A Westenius, Amgad M Ahmed Muhammad - okladka książki

Wydawnictwo:
Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
202
Dostępne formaty:
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     Mobi

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Starting from the basics of computer vision and OpenCV, we'll take you all the way to creating exciting applications. You will discover that, though computer vision is a challenging subject, the ideas and algorithms used are simple and intuitive, and you will appreciate the abstraction layer that OpenCV uses to do the heavy lifting for you. Packed with many examples, the book will help you understand the main data structures used within OpenCV, and how you can use them to gain performance boosts. Next we will discuss and use several image processing algorithms such as histogram equalization, filters, and color space conversion. You then will learn about image gradients and how they are used in many shape analysis techniques such as edge detection, Hough Line Transform, and Hough Circle Transform. In addition to using shape analysis to find things in images, you will learn how to describe objects in images in a more robust way using different feature detectors and descriptors.
By the end of this book, you will be able to make intelligent decisions using the famous Adaboost learning algorithm.

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O autorach książki

OpenCV
Amgad Muhammad has a master's degree in computer science from the American University in Cairo. He is a data scientist passionate about building and designing machine learning models and engineering features in computer vision research. He started working with computer vision in 2011, and now he is developing state-of-the-art machine learning and computer vision algorithms in the field of biometric extraction and registration, including face and person detection, human tracking, and 2D/3D-based pose estimation.

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