Overview
- 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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Table of contents (6 chapters)
Keywords
About this book
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.
Authors and Affiliations
About the author
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.
Bibliographic Information
Book Title: Learn Computer Vision Using OpenCV
Book Subtitle: With Deep Learning CNNs and RNNs
Authors: Sunila Gollapudi
DOI: https://doi.org/10.1007/978-1-4842-4261-2
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: Sunila Gollapudi 2019
Softcover ISBN: 978-1-4842-4260-5Published: 27 April 2019
eBook ISBN: 978-1-4842-4261-2Published: 26 April 2019
Edition Number: 1
Number of Pages: XX, 151
Number of Illustrations: 27 b/w illustrations, 61 illustrations in colour
Topics: Artificial Intelligence, Python, Open Source