| Description |
1 online resource : illustrations. |
| Physical Medium |
polychrome |
| Description |
text file |
| Series |
IET computing series ; 42
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IET computing series ; 42.
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| Contents |
Intro -- Title -- Copyright -- Contents -- About the editors -- Preface -- 1 Computer vision and recognition-based safe automated systems -- 1.1 Introduction -- 1.1.1 Role of computer vision in automation -- 1.1.2 Organization of the chapter -- 1.2 Literature survey of safe automation systems -- 1.3 Application of computer vision technology in automation -- 1.3.1 Using face ID in mobile devices -- 1.3.2 Automated automobiles -- 1.3.3 Computer vision in agriculture -- 1.3.4 Computer vision in the health sector -- 1.3.5 Computer vision in the e-commerce industry -- 1.3.6 Generating 3D maps |
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1.3.7 Classifying and detecting objects -- 1.3.8 Congregation data for training algorithms -- 1.3.9 Low-light mode with computer vision -- 1.4 Ensuring safety during COVID-19 using computer vision -- 1.4.1 AI started from bringing humans closer to forcing them in keeping apart -- 1.4.2 Access control through computer vision -- 1.4.3 Thermal fever detection cameras -- 1.4.4 Social distancing detection -- 1.4.5 Sanitization prioritization -- 1.4.6 Face mask compliance -- 1.5 Discussion and conclusion -- References -- 2 DLA: deep learning accelerator -- 2.1 Introduction |
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2.2 ASIC-based design accelerator -- 2.3 FPGA-based design accelerator -- 2.4 NoC-based design accelerator -- 2.5 Flow mapping and its impact on DLAs__amp__#8217 -- performance -- 2.6 A heuristic or dynamic algorithm__amp__#8217 -- s role on a DLA__amp__#8217 -- s efficiency -- 2.7 Brief state-of-the-art survey -- References -- 3 Intelligent image retrieval system using deep neural networks -- 3.1 Introduction -- 3.2 Conventional content-based image retrieval (CBIR) system -- 3.2.1 Semantic-based image retrieval (SBIR) system -- 3.3 Deep learning |
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3.4 Image retrieval using convolutional neural networks (CNN) -- 3.5 Image retrieval using autoencoders -- 3.6 Image retrieval using generative adversarial networks (GAN) -- References -- 4 Handwritten digits recognition using dictionary learning -- 4.1 Introduction -- 4.1.1 Optical character recognition -- 4.1.2 Handwritten recognition -- 4.2 Related works -- 4.3 Dictionary learning -- 4.4 DPL variants for HNR -- 4.4.1 Dictionary pair learning model -- 4.4.2 Incoherent dictionary pair learning (InDPL) -- 4.4.3 Labeled projective dictionary pair learning -- 4.5 Input data preparation |
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4.5.1 Image preprocessing -- 4.5.2 Histogram of oriented gradient -- 4.5.3 Classification stage -- 4.6 HNR datasets -- 4.7 Experimental results -- 4.7.1 Cross-validation -- 4.7.2 Benchmarking results -- 4.8 Conclusions -- References -- 5 Handwriting recognition using CNN and its optimization approach -- 5.1 Introduction -- 5.2 Related works -- 5.3 Background -- 5.3.1 Convolutional neural network -- 5.3.2 Gated convolutional neural network -- 5.3.3 Gated recurrent unit (GRU) -- 5.3.4 Connectionist temporal classification (CTC) -- 5.3.5 Residual operation -- 5.3.6 Bi-directional gated recurrent unit (BiGRU) |
| Local Note |
eBooks on EBSCOhost EBSCO eBook Subscription Academic Collection - North America |
| Subject |
Computer vision.
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Computer vision. |
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Machine learning.
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Machine learning. |
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Pattern recognition systems.
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Pattern recognition systems. |
| Genre/Form |
Electronic books.
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| Added Author |
Chowdhary, Chiranji Lal, 1975- editor.
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Alazab, Mamoun, 1980- editor.
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Chaudhary, Ankit, editor.
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Hakak, Saqib, editor.
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Gadekallu, Thippa Reddy, editor.
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| Other Form: |
Print version: Computer vision and recognition systems using machine and deep learning approaches. Stevenage : Institution of Engineering and Technology, 2021 9781839533235 (OCoLC)1272894888 |
| ISBN |
1839533242 (electronic book) |
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9781839533242 (electronic book) |
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9781839533235 (hardback) |
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