| Description |
1 online resource : illustrations |
| Series |
IET computing series ; 42 |
|
IET computing series ; 42
|
| 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 |
|
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 |
|
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 |
|
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 |
|
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) |
| Summary |
Computer vision is an interdisciplinary scientific field that deals with how computers obtain, store, interpret and understand digital images or videos using artificial intelligence based on neural networks, machine learning and deep learning methodologies. They are used in countless applications such as image retrieval and classification, driving and transport monitoring, medical diagnostics and aerial monitoring. Written by a team of international experts, this edited book covers the state-of-the-art of advanced research in the fields of computer vision and recognition systems from fundamental concepts to methodologies and technologies and real world applications including object detection, biometrics, Deepfake detection, sentiment and emotion analysis, traffic enforcement camera monitoring, vehicle control and aerial remote sensing imagery. The book will be useful for industry and academic researchers, scientists and engineers in the fields of computer vision, machine vision, image processing and recognition, multimedia, AI, machine and deep learning, data science, biometrics, security, and signal processing. It will also make a great course reference for advanced students and lecturers in these fields of research |
| Notes |
Online resource; title from PDF title page (IET Digital Library, viewed on November 24, 2021) |
| Subject |
Computer vision.
|
|
Machine learning.
|
|
Pattern recognition systems.
|
|
Pattern Recognition, Automated
|
|
Machine Learning
|
|
Computer vision
|
|
Machine learning
|
|
Pattern recognition systems
|
| Form |
Electronic book
|
| Author |
Chowdhary, Chiranji Lal, 1975- editor.
|
|
Alazab, Mamoun, 1980- editor.
|
|
Chaudhary, Ankit, editor
|
|
Hakak, Saqib, editor
|
|
Gadekallu, Thippa Reddy, editor
|
| ISBN |
9781839533242 |
|
1839533242 |
|
