Deep Learning in Action: Image and Video Processing for Practical Use

Erscheinungsjahr 2025
ISBN: 978-0-443-30078-3
Verlag: Elsevier Science & Technology

Buch, Englisch, Format (B × H): 191 mm x 235 mm, Gewicht: 620 g

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 general methodological contributions from AI, such as a variety of recently developed deep learning algorithms, have also been applied to a wide spectrum of fields such as surveillance applications, real-time processing, IoT devices, and health care systems. The state-of-the-art and deep learning models have wider applicability and are highly efficient. Deep Learning in Action: Image and Video Processing for Practical Use provides a comprehensive and accessible resource for both intermediate to advanced readers seeking to harness the power of deep learning in the domains of video and image processing. The book bridges the gap between theoretical concepts and practical implementation by emphasizing lightweight approaches, enabling readers to efficiently apply deep learning techniques to real-world scenarios. It focuses on resource-efficient methods, making it particularly relevant in contexts where computational constraints are a concern.

Autoren/Hrsg.

Elhanashi, Abdussalam

Saponara, Sergio

Fachgebiete

• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Signalverarbeitung, Bildverarbeitung, Scanning
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung

Inhaltsverzeichnis

1. Introduction
2. Image Analysis for Surveillance: Detecting Fire and Smoke Incidents
3. Enhancing COVID-19 Safety Measures with AI-Powered Video Analysis
4. Deep Learning Approaches for Fingerprint Image Restoration 4.1 Background
5. Deep Learning for Classification and Localization of Multiple Abnormalities on chest x-rays images
6. Real-time Stroke Detection Based on Deep Learning and Federated Learning
7. Efficient Identification of Bag Breakup in Continuous Airflow via Video Analysis

Über Autor(innen)

Saponara, Sergio
Sergio Saponara, Director of the Department of Information Engineering at the University of Pisa, IEEE Distinguished Lecturer, obtained his Master's degree cum laude and Ph.D. in Electronic Engineering from the University of Pisa. In 2002, he was a Marie Curie Research Fellow at the Inter-university Microelectronics Center (IMEC) in Leuven, Belgium. He was also a post-graduate researcher at the National Research Council. Currently, he is a Full Professor of Electronics at the University of Pisa, where he teaches courses in Vehicular Electronics, Electronic Systems for Robotics, HW and Embedded Security for the Master's degrees in Vehicle Engineering, Robotics and Automation Engineering, and Cybersecurity. Additionally, he teaches Electronics at the Italian Naval Academy in Livorno. Prof. Saponara was President of the Bachelor's and Master's programs in Electronic Engineering at the University of Pisa. He has co-authored 400 scientific articles indexed in Scopus and 20 patents. He is a Founding Member of the IoT CASS SiG and has been a Program Committee Member for over 100 international IEEE and SPIE conferences. He also serves as the Director of the summer school "Enabling Technologies for the Internet of Things (IoT)" and the specialization course "Automotive Electronics and Powertrain Electrification." He is also the Director of the interUniversity Center for Automotive Research (UCAR).

Elhanashi, Abdussalam
Abdussalam Elhanashi, PhD, is a Senior AI Researcher, and an IEEE Senior Member, with expertise in artificial intelligence, deep learning, computer vision, image and video processing, intelligent surveillance, IoT, and edge computing. He holds a Ph.D. in Information Engineering from the University of Pisa, an M.Sc. from the University of Glasgow, and an MBA from the University of Nicosia.

His research focuses on the development and real-world deployment of intelligent systems across healthcare, surveillance, industrial, and IoT-enabled applications, with particular emphasis on computer vision, video analytics, medical imaging, lightweight deep learning, embedded and edge AI, and efficient image and video processing. He has conducted research collaborations with the University of Strathclyde in the UK and Hiroshima University in Japan, contributing to multidisciplinary research in AI and intelligent systems. He contributes to imaging informatics through the Society for Imaging Informatics in Medicine (SIIM) and serves as an editor and reviewer for international scientific journals. He is the author of four technical books published by Elsevier, Springer, and MDPI.