A Guide to Convolutional Neural Networks for Computer Vision (Record no. 86132)

000 -LEADER
fixed length control field 04359nam a22005655i 4500
001 - CONTROL NUMBER
control field 978-3-031-01821-3
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240730165145.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 220601s2018 sz | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783031018213
-- 978-3-031-01821-3
082 04 - CLASSIFICATION NUMBER
Call Number 006
100 1# - AUTHOR NAME
Author Khan, Salman.
245 12 - TITLE STATEMENT
Title A Guide to Convolutional Neural Networks for Computer Vision
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2018.
300 ## - PHYSICAL DESCRIPTION
Number of Pages XIX, 187 p.
490 1# - SERIES STATEMENT
Series statement Synthesis Lectures on Computer Vision,
505 0# - FORMATTED CONTENTS NOTE
Remark 2 Preface -- Acknowledgments -- Introduction -- Features and Classifiers -- Neural Networks Basics -- Convolutional Neural Network -- CNN Learning -- Examples of CNN Architectures -- Applications of CNNs in Computer Vision -- Deep Learning Tools and Libraries -- Conclusion -- Bibliography -- Authors' Biographies.
520 ## - SUMMARY, ETC.
Summary, etc Computer vision has become increasingly important and effective in recent years due to its wide-ranging applications in areas as diverse as smart surveillance and monitoring, health and medicine, sports and recreation, robotics, drones, and self-driving cars. Visual recognition tasks, such as image classification, localization, and detection, are the core building blocks of many of these applications, and recent developments in Convolutional Neural Networks (CNNs) have led to outstanding performance in these state-of-the-art visual recognition tasks and systems. As a result, CNNs now form the crux of deep learning algorithms in computer vision. This self-contained guide will benefit those who seek to both understand the theory behind CNNs and to gain hands-on experience on the application of CNNs in computer vision. It provides a comprehensive introduction to CNNs starting with the essential concepts behind neural networks: training, regularization, and optimization of CNNs.The book also discusses a wide range of loss functions, network layers, and popular CNN architectures, reviews the different techniques for the evaluation of CNNs, and presents some popular CNN tools and libraries that are commonly used in computer vision. Further, this text describes and discusses case studies that are related to the application of CNN in computer vision, including image classification, object detection, semantic segmentation, scene understanding, and image generation. This book is ideal for undergraduate and graduate students, as no prior background knowledge in the field is required to follow the material, as well as new researchers, developers, engineers, and practitioners who are interested in gaining a quick understanding of CNN models.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
General subdivision Digital techniques.
700 1# - AUTHOR 2
Author 2 Rahmani, Hossein.
700 1# - AUTHOR 2
Author 2 Shah, Syed Afaq Ali.
700 1# - AUTHOR 2
Author 2 Bennamoun, Mohammed.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1007/978-3-031-01821-3
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
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-- Cham :
-- Springer International Publishing :
-- Imprint: Springer,
-- 2018.
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-- text
-- txt
-- rdacontent
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-- computer
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-- rdamedia
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-- online resource
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-- rdacarrier
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-- text file
-- PDF
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650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Image processing
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computer vision.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Pattern recognition systems.
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computer Imaging, Vision, Pattern Recognition and Graphics.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computer Vision.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Automated Pattern Recognition.
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
-- 2153-1064
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-- ZDB-2-SXSC

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