Learn Computer Vision Using Opencv: With Deep Learning CNN and Rnn

Name: Learn Computer Vision Using Opencv: With Deep Learning CNN and Rnn
Brand: Apress
SKU: 9781484242605
Price: 4040.0 INR
Availability: InStock

by Sunila Gollapudi

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ISBN13: 9781484242605
Binding: Paperback
Subject: N/A
Publisher: Apress
Publisher Imprint: Apress
Publication Date: April 27, 2019
Pages: 115
Original Price: EUR 54.99
Language: English
Edition: N/A
Item Weight: 250 grams
BISAC Subject(s): Computer Science, Languages / Python, and Programming / Open Source

About The Book

From the Back Cover
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.
You will:

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

About The Author

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.

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