Computer vision and recognition systems using machine and deep learning approaches : fundamentals, technologies and applications

Computer vision and recognition systems using machine and deep learning approaches : fundamentals, technologies and applications /

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Bibliographic Details
Corporate Author: ProQuest (Firm)
Other Authors: Chowdhary, Chiranji Lal, 1975- (Editor), Alazab, Mamoun, 1980- (Editor), Chaudhary, Ankit (Editor), Hakak, Saqib (Editor), Gadekallu, Thippa Reddy (Editor)
Format: Electronic eBook
Language:English
Published: Stevenage : Institution of Engineering and Technology, 2021.
Series:IET computing series ; 42.
Subjects:
Computer vision.
Machine learning.
Pattern recognition systems.
Online Access:Connect to this title online (unlimited simultaneous users allowed; 325 uses per year)
Table of 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)

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