國立虎尾科技大學 |

Learn Computer Vision Using OpenCV = With Deep Learning CNNs and RNNs /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Learn Computer Vision Using OpenCV/ by Sunila Gollapudi.
Reminder of title:	With Deep Learning CNNs and RNNs /
Author:	Gollapudi, Sunila.
Description:	XX, 151 p. 88 illus., 61 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence. -
Online resource:	https://doi.org/10.1007/978-1-4842-4261-2
ISBN:	9781484242612

Learn Computer Vision Using OpenCV = With Deep Learning CNNs and RNNs /
Gollapudi, Sunila.

Learn Computer Vision Using OpenCVWith Deep Learning CNNs and RNNs /[electronic resource] :by Sunila Gollapudi. - 1st ed. 2019. - XX, 151 p. 88 illus., 61 illus. in color.online resource.

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

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

559380
Artificial intelligence.

LC Class. No.: Q334-342

Dewey Class. No.: 006.3

Learn Computer Vision Using OpenCV = With Deep Learning CNNs and RNNs /
LDR:03229nam a22003855i 4500 001 1011278
003 DE-He213
005 20200701232756.0
007 cr nn 008mamaa
008 210106s2019 xxu| s |||| 0|eng d
020 $a 9781484242612 $9 978-1-4842-4261-2
024 7 $a 10.1007/978-1-4842-4261-2 $2 doi
035 $a 978-1-4842-4261-2
050 4 $a Q334-342
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
100 1 $a Gollapudi, Sunila. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1226313
245 1 0 $a Learn Computer Vision Using OpenCV $h [electronic resource] : $b With Deep Learning CNNs and RNNs / $c by Sunila Gollapudi.
250 $a 1st ed. 2019.
264 1 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2019.
300 $a XX, 151 p. 88 illus., 61 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
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.
650 0 $a Artificial intelligence. $3 559380
650 0 $a Python (Computer program language). $3 1127623
650 0 $a Open source software. $3 561177
650 0 $a Computer programming. $3 527822
650 1 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Python. $3 1115944
650 2 4 $a Open Source. $3 1113081
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9781484242605
776 0 8 $i Printed edition: $z 9781484242629
856 4 0 $u https://doi.org/10.1007/978-1-4842-4261-2
912 $a ZDB-2-CWD
912 $a ZDB-2-SXPC
950 $a Professional and Applied Computing (SpringerNature-12059)
950 $a Professional and Applied Computing (R0) (SpringerNature-43716)