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Deep learning in action : image and video processing for practical use /

Artificial intelligence technology has entered an extraordinary phase of fast development and wide application. The techniques developed in traditional AI research areas, such as computer vision and object recognition, have found many innovative applications in an array of real-world settings. The g...

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Bibliographic Details
Main Author: Elhanashi, Abdussalam (Author)
Corporate Author: ScienceDirect (Online service)
Other Authors: Saponara, Sergio
Format: eBook
Language:English
Published: Amsterdam : Elsevier, 2025.
Subjects:
Deep learning (Machine learning)
Image processing.
Digital video.
Apprentissage profond.
Traitement d'images.
Vidéo numérique.
image processing.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Front Cover
  • Deep Learning in Action: Image and Video Processing for Practical Use
  • Copyright Page
  • Dedication
  • Contents
  • Foreword
  • Preface
  • Acknowledgments
  • Nomenclature
  • 1 Introduction
  • 1.1 Overview
  • 1.1.1 Definition and evolution of artificial intelligence
  • 1.1.2 Machine learning fundamental
  • 1.1.3 Machine learning types
  • 1.2 The power of deep learning
  • 1.2.1 Deep learning fundamental
  • 1.2.2 Deep neural network
  • 1.2.3 Convolutional neural network
  • 1.2.4 Overview of object detection, classification, and segmentation
  • 1.2.5 Autoencoders
  • 1.2.6 Computer vision applications
  • 1.2.7 Deep learning for embedded systems
  • 1.3 Understanding image and video data
  • 1.3.1 Overview of the importance of image and video data in various real-world applications
  • 1.3.2 The evolution of visual processing
  • 1.3.3 The differentiating between image and video data
  • 1.3.4 The common image formats and their properties
  • 1.4 Importance of real-world applications
  • 1.4.1 Bridging the gap between theory and practice
  • 1.4.2 Solving real-world challenges
  • 1.4.3 Industry relevance and innovation
  • 1.5 Book outline
  • References
  • 2 Image analysis for surveillance: detecting fire and smoke incidents
  • 2.1 State-of-the-art video-based fire/smoke detection
  • 2.1.1 Extraction of the regional proposal
  • 2.1.2 Run-time object detection
  • 2.1.3 Experiment set up and results
  • 2.1.4 Size of detected regions
  • 2.1.5 Testing the R-CNN with Raspberry Pi
  • 2.2 Real-time fire and smoke detection
  • 2.2.1 Methodology and results
  • 2.2.2 Execution the protype on NVIDIA Jetson Nano
  • References
  • 3 Enhancing COVID-19 safety measures with AI-powered video analysis
  • 3.1 Introduction
  • 3.2 Related work
  • 3.3 Social distancing using YOLOv2
  • 3.3.1 Social distancing workflow.
  • 3.3.1.1 Preparation of thermal images or video streaming
  • 3.3.1.2 Application of deep learning object detector
  • 3.3.1.3 Verification of people count
  • 3.3.1.4 Calculation of interpersonal distances
  • 3.3.1.5 Algorithmic decision-making
  • 3.3.2 YOLOv2 architecture
  • 3.3.2.1 Input layer
  • 3.3.2.2 Middle layers
  • 3.3.2.3 Feature extraction layer
  • 3.3.2.4 YOLOv2 subnetwork layers
  • 3.3.3 Experimental setup
  • 3.3.4 Euclidean formula for measuring the distance
  • 3.3.5 Results and discussion
  • 3.4 Social distancing with YOLOv4-tiny
  • 3.4.1 The proposed approach
  • 3.4.2 Violation threshold
  • 3.4.3 Bird's-eye view transformation
  • 3.4.4 Experiment setup and results
  • 3.5 Algorithms implementation on the embedded system
  • 3.5.1 Social distancing on NVIDIA devices
  • 3.5.2 Distributed video infrastructure for social distancing
  • 3.6 Integrated approach for monitoring social distancing, face mask, and facial temperature measurement
  • 3.6.1 Dataset and annotation of face masks
  • 3.6.2 Dataset and annotation of facial temperature
  • 3.6.3 Experimental setup and results
  • 3.6.4 Implementation of the integrated algorithms on NVIDIA platforms
  • References
  • 4 Deep learning approaches for fingerprint image restoration
  • 4.1 Background on biometric identification
  • 4.1.1 Deep learning related work for fingerprint
  • 4.2 Feature extraction
  • 4.3 Fingerprint dataset
  • 4.3.1 Dataset I
  • 4.3.2 Dataset II
  • 4.3.3 Dataset III
  • 4.3.4 Dataset IV
  • 4.4 Sparse autoencoder for image reconstruction
  • 4.4.1 Sparse autoencoder model
  • 4.4.2 Preprocessing the image
  • 4.4.3 Algorithm description
  • 4.4.4 Experiment setup
  • 4.4.5 Efficiency and parameter sensitivity
  • 4.5 Recreating fingerprint images by convolutional neural network
  • 4.5.1 Convolution neural network for image reconstruction.
  • 4.5.2 Convolutional neural network algorithm design
  • 4.5.3 Training and validation
  • 4.6 Experiment results and discussion
  • 4.6.1 Evaluation
  • 4.6.2 Comparative analysis
  • References
  • 5 Deep learning for classification and localization of multiple abnormalities on chest X-ray images
  • 5.1 Overview of diagnosis on medical images
  • 5.2 Literature review
  • 5.3 Computer vision for medical image processing
  • 5.3.1 Disease detection and diagnosis
  • 5.3.2 Anomaly identification
  • 5.3.3 Automated radiological measurements
  • 5.3.4 Image enhancement and reconstruction
  • 5.3.5 Workflow optimization
  • 5.3.6 Convolutional neural network for feature extraction for medical images
  • 5.3.7 Role of deep learning in medical imaging
  • 5.4 Dataset description
  • 5.4.1 COVID-19 radiography database
  • 5.4.2 COVID-19 SIIM-FISABIO-RSNA COVID-19 dataset
  • 5.5 Method
  • 5.5.1 Multiclassification of abnormalities on chest X-ray images
  • 5.5.2 Localization of abnormalities on chest X-ray images
  • 5.5.3 Ensembled models for enhancing localization of abnormalities
  • References
  • 6 Real-time stroke detection based on deep learning and federated learning
  • 6.1 Background
  • 6.1.1 Stroke as a critical health issue
  • 6.1.2 Current challenges in stroke detection
  • 6.1.3 The need for real-time detection
  • 6.2 Federated learning for healthcare
  • 6.2.1 Understanding federated learning
  • 6.2.2 Privacy and security concerns in federated learning
  • 6.2.3 Benefits and challenges
  • 6.3 Real-time stroke detection system
  • 6.3.1 Design and architecture
  • 6.3.2 Data preprocessing and augmentation
  • 6.3.3 Distributed model and federated learning
  • 6.3.4 Training setup and optimization
  • 6.3.5 Experiment results and discussion
  • 6.4 Implementation on NVIDA platforms
  • 6.4.1 The importance of NVIDIA GPUs for real-time inference.
  • 6.4.2 NVIDIA computation cost analysis
  • 6.4.2.1 Power consumption trends
  • 6.4.2.2 Temperature analysis across platforms
  • 6.4.2.3 Analysis of computation cost
  • 6.4.2.3.1 Jetson Nano
  • 6.4.2.3.2 Jetson Xavier AGX
  • 6.4.2.3.3 Jetson Orin
  • References
  • 7 Efficient identification of bag-breakup in continuous airflow via video analysis
  • 7.1 Overview of bag break detection
  • 7.1.1 Bag-breakup in continuous airflow
  • 7.1.2 Factors affecting bag-breakup
  • 7.1.2.1 Fluid dynamics and flow characteristics
  • 7.1.2.2 Droplet properties
  • 7.1.2.3 Environmental conditions
  • 7.1.2.4 Combustion characteristics
  • 7.1.2.5 Impact of engine design and operating conditions
  • 7.1.3 Importance of bag-breakup detection in automotive safety
  • 7.1.4 Challenges in identifying bag-breakup
  • 7.2 Methodology
  • 7.2.1 Dataset collection and description
  • 7.2.2 Data preprocessing
  • 7.2.3 The proposed deep-learning models
  • 7.2.4 Training and validation
  • 7.3 Experimental results
  • 7.3.1 Evaluation metrices
  • 7.3.2 Comparative analysis
  • 7.3.3 Error analysis and misdetection cases
  • References
  • 8 Conclusions and recommendations
  • Glossary
  • Index
  • Back Cover.