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020 _a9783031018213
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024 7 _a10.1007/978-3-031-01821-3
_2doi
050 4 _aTA1501-1820
050 4 _aTA1634
072 7 _aUYT
_2bicssc
072 7 _aCOM016000
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082 0 4 _a006
_223
100 1 _aKhan, Salman.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987660
245 1 2 _aA Guide to Convolutional Neural Networks for Computer Vision
_h[electronic resource] /
_cby Salman Khan, Hossein Rahmani, Syed Afaq Ali Shah, Mohammed Bennamoun.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aXIX, 187 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
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_2rda
490 1 _aSynthesis Lectures on Computer Vision,
_x2153-1064
505 0 _aPreface -- 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 _aComputer 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 _aImage processing
_xDigital techniques.
_94145
650 0 _aComputer vision.
_987663
650 0 _aPattern recognition systems.
_93953
650 1 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
_931569
650 2 4 _aComputer Vision.
_987666
650 2 4 _aAutomated Pattern Recognition.
_931568
700 1 _aRahmani, Hossein.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987668
700 1 _aShah, Syed Afaq Ali.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987670
700 1 _aBennamoun, Mohammed.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987672
710 2 _aSpringerLink (Online service)
_987675
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031000782
776 0 8 _iPrinted edition:
_z9783031006937
776 0 8 _iPrinted edition:
_z9783031029493
830 0 _aSynthesis Lectures on Computer Vision,
_x2153-1064
_987676
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01821-3
912 _aZDB-2-SXSC
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999 _c86132
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