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Learn computer vision using OpenCVwi...

Gollapudi, Sunila.

Learn computer vision using OpenCVwith deep learning CNNs and RNNs /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Learn computer vision using OpenCVby Sunila Gollapudi.
Reminder of title:	with deep learning CNNs and RNNs /
Author:	Gollapudi, Sunila.
Published:	Berkeley, CA :Apress :2019.
Description:	xx, 151 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Computer vision.
Online resource:	https://doi.org/10.1007/978-1-4842-4261-2
ISBN:	9781484242612$q(electronic bk.)

Learn computer vision using OpenCVwith deep learning CNNs and RNNs /
Gollapudi, Sunila.

Learn computer vision using OpenCVwith deep learning CNNs and RNNs /[electronic resource] :by Sunila Gollapudi. - Berkeley, CA :Apress :2019. - xx, 151 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9781484242612$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-4261-2doiSubjects--Topical Terms:

200113
Computer vision.

LC Class. No.: TA1634 / .G65 2019

Dewey Class. No.: 006.37

Learn computer vision using OpenCVwith deep learning CNNs and RNNs /
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505 0 $a 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.
520 $a 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.
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