Learn Computer Vision Using OpenCV, 1st ed. With Deep Learning CNNs and RNNs
Langue : Anglais
Auteur : Gollapudi Sunila
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
Those who have a basic understanding of machine learning and Python and are looking to learn computer vision and its applications.
Chapter 1: Artificial Intelligence and Computer Vision.- Chapter 2: OpenCV with Python.- Chapter 3: Deep learning for Computer Vision.- Chapter 4: Image Manipulation and Segmentation.- Chapter 5 : Object Detection and Recognition.- Chapter 6: Motion Analysis and Tracking.
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.
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
Date de parution : 04-2019
Ouvrage de 151 p.
15.5x23.5 cm
Thèmes de Learn Computer Vision Using OpenCV :
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