Computer Vision and Recognition Systems Using Machine and Deep Learning Approaches: Fundamentals, technologies and applications

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)
        5.3.7 Squeeze and excited network (SENet)
        5.3.8 Linear bottleneck network
        5.3.9 Encoder and decoder model
    5.4 Methodology
        5.4.1 Data gathering
        5.4.2 Preprocessing
        5.4.3 Model overview
        5.4.4 Metrics
        5.4.5 Training configurations
        5.4.6 Unseen testing
        5.4.7 Inference time testing
        5.4.8 Visualize inside the model
    5.5 Experiments
        5.5.1 Experiment 1: Bluche versus Puigcerver versus Flor model
        5.5.2 Experiment 2: performance comparison of the encoder
        5.5.3 Experiment 3: performance comparison of the decoder
        5.5.4 Experiment 4: performance comparison of the skipped connection
        5.5.5 Experiment 5: performance comparison of other ResFlor model
        5.5.6 Experiment 6: ResFlor residual with SE network
        5.5.7 Experiment 7: ResFlor with residual and bottleneck network
    5.6 Summary
    5.7 Conclusion and future work
    Acknowledgments
    References
6 Real-time face mask detection on edge IoT devices
    6.1 IoT devices and object detection
        6.1.1 IoT devices and object detection
        6.1.2 Real-time object detection on edge IoT devices
        6.1.3 A generic detection algorithm
    6.2 Literature survey
    6.3 Traditional feature extraction techniques
        6.3.1 Histogram of oriented gradients (HOG)
        6.3.2 Scale invariant feature transform (SIFT)
        6.3.3 Speeded up robust features (SURF)
    6.4 Traditional detection methods
        6.4.1 Histogram of oriented gradients with support vector machines (HOG + SVM)
    6.5 Traditional face detection techniques
        6.5.1 Viola__amp__#8211;Jones Haar cascade method
    6.6 Face mask detection
    6.7 Deep learning for object detection
        6.7.1 Convolutional neural networks (CNNs)
        6.7.2 Object detection using deep learning
        6.7.3 Faster RCNN for object detection
        6.7.4 Enhancing faster RCNN with MobileNet
    6.8 Internet and deep learning
        6.8.1 Client__amp__#8211;server architecture
    6.9 Edge IoT architecture
    6.10 Implementing an edge IoT architecture
        6.10.1 Dynamic web pages
        6.10.2 Backend using Node.js
        6.10.3 MongoDB as database
    6.11 Discussion
    6.12 Conclusion
    References
7 Current challenges and applications of DeepFake systems
    7.1 Introduction to DeepFake
        7.1.1 Scenario
    7.2 Various DeepFake detection methods available and their limitations
        7.2.1 Traditional detection methods
        7.2.2 Methods based on deep learning
    7.3 Applications used to forge the multimedia
    7.4 Current challenges and future of the technology
        7.4.1 Quality of DeepFake dataset
        7.4.2 Performance evaluation
        7.4.3 Explainability of detection results
        7.4.4 Temporal aggregation
        7.4.5 Social media laundering
    7.5 Conclusion
    References
8 Vehicle control system based on eye, iris, and gesture recognition with eye tracking
    8.1 Introduction
    8.2 Eye tracking
        8.2.1 How eye tracker works
    8.3 Human gesture
        8.3.1 Head movement
    8.4 Applications of eye tracking
    8.5 Top eye tracking hardware companies
    8.6 Case studies
    8.7 Conclusion
    References
9 Sentiment analysis using deep learning
    9.1 Sentiment analysis: an interesting problem
    9.2 Sentiment and opinions
    9.3 Components of opinion
        9.3.1 Levels in sentiment analysis
        9.3.2 Classification techniques
        9.3.3 Classification types
    9.4 Deep learning
    9.5 Machine learning
    9.6 Traditional learning
    9.7 Hybrid learning approaches
    9.8 Deep neural networks
        9.8.1 Deep belief network
        9.8.2 Convolutional neural networks
        9.8.3 Stacked autoencoders
    9.9 Convolutional neural networks
        9.9.1 Word embeddings
        9.9.2 Bag of words (BOW)
        9.9.3 ConvNet structure
    9.10 Proposed model
        9.10.1 Datasets and experimental setup
        9.10.2 Results
        9.10.3 Effect of filter region size
        9.10.4 Effect of number of filters
        9.10.5 Effect of different classifiers
    9.11 Conclusions and future scope
    References
10 Classification of prefeature extracted images with deep convolutional neural network in facial emotion recognition of vehicle driver
    10.1 Introduction and related work
    10.2 Proposed models
        10.2.1 Datasets
        10.2.2 Preprocessing
        10.2.3 Prefeature extraction
        10.2.4 Convolutional neural networks
        10.2.5 Model design
        10.2.6 Metrics
        10.2.7 System configuration
    10.3 Experiments and results
    10.4 Vehicle driver emotion recognition experimental setup, results, and discussion
    10.5 Conclusion
    Acknowledgments
    References
11 MobileNet architecture and its application to computer vision
    11.1 Introduction
    11.2 Preliminaries
        11.2.1 Artificial neural network
        11.2.2 Convolution neural network
        11.2.3 Deep convolution neural network
    11.3 Benchmarked convolutional neural network
        11.3.1 VGG16
        11.3.2 Inception v3
    11.4 MobileNet architecture
        11.4.1 MobileNetv1
        11.4.2 MobileNetv2
        11.4.3 MobileNetv3
        11.4.4 NASNet mobile
    11.5 Model optimization techniques
        11.5.1 Quantization technique
    11.6 Quantized deep convolutional neural network
        11.6.1 Methodology
    11.7 Case study: healthcare domain
        11.7.1 Diabetic retinopathy
        11.7.2 Kaggle diabetic retinopathy image datasets
        11.7.3 Approach
        11.7.4 Experiment results and discussion
        11.7.5 Conclusion
    11.8 Selected MobileNet application
        11.8.1 Image classification
        11.8.2 Object detection
        11.8.3 Segmentation
    11.9 Future direction
    References
12 Study on traffic enforcement cameras monitoring to detect the wrong-way movement of vehicles using deep convolutional neural network
    12.1 Introduction
    12.2 Background
    12.3 Techniques for data collection
        12.3.1 Closed-circuit television
        12.3.2 Manual videos
    12.4 Purpose and benefit of the cameras monitoring system
    12.5 Techniques used in the monitoring of vehicles
        12.5.1 Convolution neural network
        12.5.2 R-CNN
        12.5.3 Fast region-based convolution neural network
        12.5.4 Faster R-CNN
        12.5.5 Single-shot MultiBoxDetector
        12.5.6 You Only Look Once
    12.6 Case study
        12.6.1 The detection of wrong-way drive of automobiles based on appearance using deep convolutional neural network
        12.6.2 Real-time wrong-direction detection based on deep learning
        12.6.3 A vehicle finding and counting system based on vision using deep learning
        12.6.4 A highway automobile discovery algorithm based on CNN
        12.6.5 Comparison of case studies
    12.7 Conclusion
    References
13 Glasses for smart tourism applications
    13.1 Introduction
        13.1.1 Motivation
        13.1.2 Contribution of our work
    13.2 Article structure
    13.3 Existing technologies related to smart glasses
        13.3.1 Applications of smart glasses
        13.3.2 Smart glasses technology in the market
        13.3.3 Smart glasses solutions papers
    13.4 System assumptions
    13.5 Functional architecture and technologies relevant
        13.5.1 Voice to text conversion-KALDI
        13.5.2 Name remember
        13.5.3 Facial features extraction
        13.5.4 Object (plant and animal) identification
        13.5.5 Text detection
        13.5.6 Text-translation
        13.5.7 Navigation
        13.5.8 Text to speech conversion
    13.6 Proposed style of interaction (KBSIS)
        13.6.1 KALDI
        13.6.2 Dataset and model used
        13.6.3 Inputs
        13.6.4 Inference from input and processing
        13.6.5 Outputs
    13.7 Results and discussion
        13.7.1 Navigation
        13.7.2 Music
        13.7.3 Text from image and Translate
        13.7.4 Remembering face and naming
        13.7.5 Face characteristics
        13.7.6 Weather
        13.7.7 Plant identification and search
        13.7.8 Animal identification and search
    13.8 Conclusion
    References
14 Renal calculi detection using modified grey wolf optimization
    14.1 Introduction
    14.2 Background
        14.2.1 Image segmentation
        14.2.2 Grey wolf optimization
        14.2.3 Previous work
    14.3 Proposed approach for renal calculi detection
        14.3.1 Challenges in renal calculi detection
        14.3.2 Proposed approach
    14.4 Experiment and results
        14.4.1 Dataset
        14.4.2 Performance analysis
    14.5 Conclusions and future scope
    References
15 On multi-class aerial image classification using learning machines
    15.1 Introduction
    15.2 Learning approaches
        15.2.1 Deep learning networks
        15.2.2 Feature learning
        15.2.3 Challenges for deep learning
        15.2.4 Challenges related to aerial video classification
        15.2.5 Applications
    15.3 Learning architecture and classification
        15.3.1 Supervised learning architectures
        15.3.2 Unsupervised learning
        15.3.3 Deep learning for planning and situational awareness
        15.3.4 Deep learning for motion control
        15.3.5 Object detection
        15.3.6 Classification
    15.4 Training
        15.4.1 Weight initialization
        15.4.2 Convolutional methods
        15.4.3 Activation functions
        15.4.4 Subsampling or pooling layer
        15.4.5 Optimization techniques
        15.4.6 Benchmark datasets
    15.5 Energy efficiency in learning approaches
    15.6 Performance metrics
    15.7 Development kits and frameworks
    15.8 Discussions and future directions
    References
16 Machine learning methodology toward identification of mature citrus fruits
    16.1 Introduction
        16.1.1 Harvesting
        16.1.2 Farm automation
        16.1.3 Fruit detection
        16.1.4 Proposed method
    16.2 Literature survey
    16.3 Implementation
        16.3.1 Image acquisition and data collection
        16.3.2 Pre-processing
        16.3.3 Feature extraction
        16.3.4 Machine learning model and database formation
        16.3.5 Data retrieval
        16.3.6 Match with dataset or testing the model
        16.3.7 Display result
        16.3.8 Application design
    16.4 Experiments and result
        16.4.1 Qualification measures
        16.4.2 Training result
        16.4.3 Testing result
    16.5 Conclusion
        16.5.1 Future scope
    References
17 Automated detection of defects and grading of cashew kernels using machine learning
    17.1 Introduction
        17.1.1 Related work
        17.1.2 Proposed methodology
    17.2 Defects and grades of cashew kernels
        17.2.1 Cashew kernel manufacturing process
        17.2.2 Defects of cashew kernel
        17.2.3 Grades of cashew kernel
    17.3 Implementation of the methodology
        17.3.1 Image preprocessing and segmentation
        17.3.2 Feature extraction
        17.3.3 Classification
    17.4 Results and discussions
    17.5 Conclusion
    References
Index