 Название: Learn Computer Vision Using OpenCV: With Deep Learning CNNs and RNNs
Автор: Sunila Gollapudi
Издательство: Apress
ISBN: 1484242602
Год: 2019
Страниц: 163
Язык: английский
Формат: pdf (true), epub
Размер: 11.3 MB
Build practical applications of computer vision using the OpenCV library with Python. This book discusses different facets of computer vision such as image and object detection, tracking and motion analysis and their applications with examples.
The author starts with an introduction to computer vision followed by setting up OpenCV from scratch using Python. The next section discusses specialized image processing and segmentation and how images are stored and processed by a computer. This involves pattern recognition and image tagging using the OpenCV library. Next, you’ll work with object detection, video storage and interpretation, and human detection using OpenCV. Tracking and motion is also discussed in detail. The book also discusses creating complex deep learning models with CNN and RNN. The author finally concludes with recent applications and trends in computer vision.
After reading this book, you will be able to understand and implement computer vision and its applications with OpenCV using Python. You will also be able to create deep learning models with CNN and RNN and understand how these cutting-edge deep learning architectures work.
What You Will Learn:
Understand what computer vision is, and its overall application in intelligent automation systems
Discover the deep learning techniques required to build computer vision applications
Build complex computer vision applications using the latest techniques in OpenCV, Python, and NumPy
Create practical applications and implementations such as face detection and recognition, handwriting recognition, object detection, and tracking and motion analysis
Who This Book Is For:
Those who have a basic understanding of machine learning and Python and are looking to learn computer vision and its applications.
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Learn Computer Vision Using OpenCV: With Deep Learning CNNs and RNNs 
