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Learn Computer Vision Using OpenCV

With Deep Learning CNN and RNN

Authors: Gollapudi, Sunila

  • Helps readers get a jump start to computer vision implementations
  • Offers use-case driven implementation for computer vision with focused learning on OpenCV and Python libraries
  • Helps create deep learning models with CNN and RNN, and explains how these cutting-edge deep learning architectures work
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Buy this book

eBook 22,99 €
price for China (P.R.) (gross)
  • The eBook version of this title will be available soon
  • Due: 2019年6月8日
  • ISBN 978-1-4842-4261-2
  • Digitally watermarked, DRM-free
  • Included format:
  • ebooks can be used on all reading devices
Softcover 27,99 €
price for China (P.R.) (gross)
  • Due: 2019年5月11日
  • ISBN 978-1-4842-4260-5
  • Free shipping for individuals worldwide
About this book

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 ForThose who have a basic understanding of machine learning and Python and are looking to learn computer vision and its applications. 

About the authors

Sunila Gollapudi has over 17 years of experience in developing, designing and architecting data-driven solutions with a focus on the banking and financial services sector. She is currently working at  Broadridge, India as vice president. She's played various roles as chief architect, big data and AI evangelist, and mentor.
She has been a speaker at various conferences and meetups on Java and big data technologies. Her current big data and data science expertise includes Hadoop, Greenplum, MarkLogic, GemFire, ElasticSearch, Apache Spark, Splunk, R, Julia, Python (scikit-learn), Weka, MADlib, Apache Mahout, and advanced analytics techniques such as deep learning, computer vision, reinforcement, and ensemble learning.

Buy this book

eBook 22,99 €
price for China (P.R.) (gross)
  • The eBook version of this title will be available soon
  • Due: 2019年6月8日
  • ISBN 978-1-4842-4261-2
  • Digitally watermarked, DRM-free
  • Included format:
  • ebooks can be used on all reading devices
Softcover 27,99 €
price for China (P.R.) (gross)
  • Due: 2019年5月11日
  • ISBN 978-1-4842-4260-5
  • Free shipping for individuals worldwide

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Bibliographic Information

Bibliographic Information
Book Title
Learn Computer Vision Using OpenCV
Book Subtitle
With Deep Learning CNN and RNN
Authors
Copyright
2019
Publisher
Apress
Copyright Holder
Sunila Gollapudi
eBook ISBN
978-1-4842-4261-2
DOI
10.1007/978-1-4842-4261-2
Softcover ISBN
978-1-4842-4260-5
Edition Number
1
Number of Pages
XX, 151
Number of Illustrations
27 b/w illustrations, 61 illustrations in colour
Topics