| 000 -LEADER |
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| fixed length control field |
09023nam a22008531i 4500 |
| 001 - CONTROL NUMBER |
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| control field |
8295029 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
IEEE |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20200413152929.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
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| fixed length control field |
m eo d |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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| fixed length control field |
cr cn |||m|||a |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
180224s2018 caua foab 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781681730226 |
| Qualifying information |
ebook |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| Canceled/invalid ISBN |
9781681730219 |
| Qualifying information |
paperback |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| Canceled/invalid ISBN |
9781681732787 |
| Qualifying information |
hardcover |
| 024 7# - OTHER STANDARD IDENTIFIER |
|---|
| Standard number or code |
10.2200/S00822ED1V01Y201712COV015 |
| Source of number or code |
doi |
| 035 ## - SYSTEM CONTROL NUMBER |
|---|
| System control number |
(CaBNVSL)swl00408190 |
| 035 ## - SYSTEM CONTROL NUMBER |
|---|
| System control number |
(OCoLC)1024179052 |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
CaBNVSL |
| Language of cataloging |
eng |
| Description conventions |
rda |
| Transcribing agency |
CaBNVSL |
| Modifying agency |
CaBNVSL |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
TA1634 |
| Item number |
.K424 2018 |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
006.37 |
| Edition number |
23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Khan, Salman, |
| Relator term |
author. |
| 245 12 - TITLE STATEMENT |
|---|
| Title |
A guide to convolutional neural networks for computer vision / |
| Statement of responsibility, etc. |
Salman Khan, Hossein Rahmani, Syed Afaq Ali Shah, Mohammed Bennamoun. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
[San Rafael, California] : |
| Name of producer, publisher, distributor, manufacturer |
Morgan & Claypool, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2018. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
1 PDF (xix, 187 pages) : |
| Other physical details |
illustrations. |
| 336 ## - CONTENT TYPE |
|---|
| Content type term |
text |
| Source |
rdacontent |
| 337 ## - MEDIA TYPE |
|---|
| Media type term |
electronic |
| Source |
isbdmedia |
| 338 ## - CARRIER TYPE |
|---|
| Carrier type term |
online resource |
| Source |
rdacarrier |
| 490 1# - SERIES STATEMENT |
|---|
| Series statement |
Synthesis lectures on computer vision, |
| International Standard Serial Number |
2153-1064 ; |
| Volume/sequential designation |
# 15 |
| 538 ## - SYSTEM DETAILS NOTE |
|---|
| System details note |
Mode of access: World Wide Web. |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Part of: Synthesis digital library of engineering and computer science. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes bibliographical references (pages 173-184). |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
1. Introduction -- 1.1 What is computer vision? -- 1.1.1 Applications -- 1.1.2 Image processing vs. computer vision -- 1.2 What is machine learning? -- 1.2.1 Why deep learning? -- 1.3 Book overview -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
2. Features and classifiers -- 2.1 Importance of features and classifiers -- 2.1.1 Features -- 2.1.2 Classifiers -- 2.2 Traditional feature descriptors -- 2.2.1 Histogram of oriented gradients (HOG) -- 2.2.2 Scale-invariant feature transform (SIFT) -- 2.2.3 Speeded-up robust features (SURF) -- 2.2.4 Limitations of traditional hand-engineered features -- 2.3 Machine learning classifiers -- 2.3.1 Support vector machine (SVM) -- 2.3.2 Random decision forest -- 2.4 Conclusion -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
3. Neural networks basics -- 3.1 Introduction -- 3.2 Multi-layer perceptron -- 3.2.1 Architecture basics -- 3.2.2 Parameter learning -- 3.3 Recurrent neural networks -- 3.3.1 Architecture basics -- 3.3.2 Parameter learning -- 3.4 Link with biological vision -- 3.4.1 Biological neuron -- 3.4.2 Computational model of a neuron -- 3.4.3 Artificial vs. biological neuron -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
4. Convolutional neural network -- 4.1 Introduction -- 4.2 Network layers -- 4.2.1 Pre-processing -- 4.2.2 Convolutional layers -- 4.2.3 Pooling layers -- 4.2.4 Nonlinearity -- 4.2.5 Fully connected layers -- 4.2.6 Transposed convolution layer -- 4.2.7 Region of interest pooling -- 4.2.8 Spatial pyramid pooling layer -- 4.2.9 Vector of locally aggregated descriptors layer -- 4.2.10 Spatial transformer layer -- 4.3 CNN loss functions -- 4.3.1 Cross-entropy loss -- 4.3.2 SVM hinge loss -- 4.3.3 Squared hinge loss -- 4.3.4 Euclidean loss -- 4.3.5 The l1 error -- 4.3.6 Contrastive loss -- 4.3.7 Expectation loss -- 4.3.8 Structural similarity measure -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
5. CNN learning -- 5.1 Weight initialization -- 5.1.1 Gaussian random initialization -- 5.1.2 Uniform random initialization -- 5.1.3 Orthogonal random initialization -- 5.1.4 Unsupervised pre-training -- 5.1.5 Xavier initialization -- 5.1.6 ReLU aware scaled initialization -- 5.1.7 Layer-sequential unit variance -- 5.1.8 Supervised pre-training -- 5.2 Regularization of CNN -- 5.2.1 Data augmentation -- 5.2.2 Dropout -- 5.2.3 Drop-connect -- 5.2.4 Batch normalization -- 5.2.5 Ensemble model averaging -- 5.2.6 The l2 regularization -- 5.2.7 The l1 regularization -- 5.2.8 Elastic net regularization -- 5.2.9 Max-norm constraints -- 5.2.10 Early stopping -- 5.3 Gradient-based CNN learning -- 5.3.1 Batch gradient descent -- 5.3.2 Stochastic gradient descent -- 5.3.3 Mini-batch gradient descent -- 5.4 Neural network optimizers -- 5.4.1 Momentum -- 5.4.2 Nesterov momentum -- 5.4.3 Adaptive gradient -- 5.4.4 Adaptive delta -- 5.4.5 RMSprop -- 5.4.6 Adaptive moment estimation -- 5.5 Gradient computation in CNNs -- 5.5.1 Analytical differentiation -- 5.5.2 Numerical differentiation -- 5.5.3 Symbolic differentiation -- 5.5.4 Automatic differentiation -- 5.6 Understanding CNN through visualization -- 5.6.1 Visualizing learned weights -- 5.6.2 Visualizing activations -- 5.6.3 Visualizations based on gradients -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
6. Examples of CNN architectures -- 6.1 LeNet -- 6.2 AlexNet -- 6.3 Network in network -- 6.4 VGGnet -- 6.5 GoogleNet -- 6.6 ResNet -- 6.7 ResNeXt -- 6.8 FractalNet -- 6.9 DenseNet -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
7. Applications of CNNs in computer vision -- 7.1 Image classification -- 7.1.1 PointNet -- 7.2 Object detection and localization -- 7.2.1 Region-based CNN -- 7.2.2 Fast R-CNN -- 7.2.3 Regional proposal network (RPN) -- 7.3 Semantic segmentation -- 7.3.1 Fully convolutional network (FCN) -- 7.3.2 Deep deconvolution network (DDN) -- 7.3.3 DeepLab -- 7.4 Scene understanding -- 7.4.1 DeepContext -- 7.4.2 Learning rich features from RGB-D images -- 7.4.3 Pointnet for scene understanding -- 7.5 Image generation -- 7.5.1 Generative adversarial networks (GANs) -- 7.5.2 Deep convolutional generative adversarial networks (DCGANs) -- 7.5.3 Super resolution generative adversarial network (SRGAN) -- 7.6 Video-based action recognition -- 7.6.1 Action recognition from still video frames -- 7.6.2 Two-stream CNNs -- 7.6.3 Long-term recurrent convolutional network (LRCN) -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
8. Deep learning tools and libraries -- 8.1 Caffe -- 8.2 TensorFlow -- 8.3 MatConvNet -- 8.4 Torch7 -- 8.5 Theano -- 8.6 Keras -- 8.7 Lasagne -- 8.8 Marvin -- 8.9 Chainer -- 8.10 PyTorch -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
9. Conclusion -- Bibliography -- Authors' biographies. |
| 506 ## - RESTRICTIONS ON ACCESS NOTE |
|---|
| Terms governing access |
Abstract freely available; full-text restricted to subscribers or individual document purchasers. |
| 510 0# - CITATION/REFERENCES NOTE |
|---|
| Name of source |
Compendex |
| 510 0# - CITATION/REFERENCES NOTE |
|---|
| Name of source |
INSPEC |
| 510 0# - CITATION/REFERENCES NOTE |
|---|
| Name of source |
Google scholar |
| 510 0# - CITATION/REFERENCES NOTE |
|---|
| Name of source |
Google book search |
| 520 3# - 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. |
| 530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE |
|---|
| Additional physical form available note |
Also available in print. |
| 588 ## - SOURCE OF DESCRIPTION NOTE |
|---|
| Source of description note |
Title from PDF title page (viewed on February 24, 2018). |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Computer vision |
| General subdivision |
Mathematical models. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Neural networks (Computer science) |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Convolutions (Mathematics) |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
deep learning |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
computer vision |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
convolution neural networks |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
perception |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
back-propagation |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
feed-forward networks |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
image classification |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
action recognition |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
object detection |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
object tracking |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
video processing |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
semantic segmentation |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
scene understanding |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
3D processing |
| 655 #0 - INDEX TERM--GENRE/FORM |
|---|
| Genre/form data or focus term |
Electronic books. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Rahmani, Hossein, |
| Relator term |
author. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Shah, Syed Afaq Ali, |
| Relator term |
author. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Bennamoun, M. |
| Fuller form of name |
(Mohammed), |
| Relator term |
author. |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
|---|
| Relationship information |
Print version: |
| International Standard Book Number |
9781681730219 |
| -- |
9781681732787 |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE |
|---|
| Uniform title |
Synthesis digital library of engineering and computer science. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE |
|---|
| Uniform title |
Synthesis lectures on computer vision ; |
| Volume/sequential designation |
# 15. |
| International Standard Serial Number |
2153-1064 |
| 856 42 - ELECTRONIC LOCATION AND ACCESS |
|---|
| Materials specified |
Abstract with links to resource |
| Uniform Resource Identifier |
http://ieeexplore.ieee.org/servlet/opac?bknumber=8295029 |
