Deep Learning For Multimedia Processing Applications
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Deep Learning For Multimedia Processing Applications
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Author : Uzair Aslam Bhatti
language : en
Publisher: CRC Press
Release Date : 2024-02-21
Deep Learning For Multimedia Processing Applications written by Uzair Aslam Bhatti and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-21 with Computers categories.
Deep Learning for Multimedia Processing Applications is a comprehensive guide that explores the revolutionary impact of deep learning techniques in the field of multimedia processing. Written for a wide range of readers, from students to professionals, this book offers a concise and accessible overview of the application of deep learning in various multimedia domains, including image processing, video analysis, audio recognition, and natural language processing. Divided into two volumes, Volume One begins by introducing the fundamental concepts of deep learning, providing readers with a solid foundation to understand its relevance in multimedia processing. Readers will discover how deep learning techniques enable accurate and efficient image recognition, object detection, semantic segmentation, and image synthesis. The book also covers video analysis techniques, including action recognition, video captioning, and video generation, highlighting the role of deep learning in extracting meaningful information from videos. Furthermore, the book explores audio processing tasks such as speech recognition, music classification, and sound event detection using deep learning models. It demonstrates how deep learning algorithms can effectively process audio data, opening up new possibilities in multimedia applications. Lastly, the book explores the integration of deep learning with natural language processing techniques, enabling systems to understand, generate, and interpret textual information in multimedia contexts. Throughout the book, practical examples, code snippets, and real-world case studies are provided to help readers gain hands-on experience in implementing deep learning solutions for multimedia processing. Deep Learning for Multimedia Processing Applications is an essential resource for anyone interested in harnessing the power of deep learning to unlock the vast potential of multimedia data.
Deep Learning Based Applications For Multimedia Processing Applications
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Author : Uzair Aslam Bhatti
language : en
Publisher:
Release Date : 2024
Deep Learning Based Applications For Multimedia Processing Applications written by Uzair Aslam Bhatti and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024 with Deep learning (Machine learning) categories.
"Deep Learning for Multimedia Processing is a comprehensive guide that explores the revolutionary impact of deep learning techniques in the field of multimedia processing. Written for a wide range of readers, from students to professionals, this book offers a concise and accessible overview of the application of deep learning in various multimedia domains, including image processing, video analysis, audio recognition, and natural language processing. Divided into two volumes, Volume One begins by introducing the fundamental concepts of deep learning, providing readers with a solid foundation to understand its relevance in multimedia processing.Readers will discover how deep learning techniques enable accurate and efficient image recognition, object detection, semantic segmentation, and image synthesis. The book also covers video analysis techniques, including action recognition, video captioning, and video generation, highlighting the role of deep learning in extracting meaningful information from videos. Furthermore, the book explores audio processing tasks such as speech recognition, music classification, and sound event detection using deep learning models. It demonstrates how deep learning algorithms can effectively process audio data, opening up new possibilities in multimedia applications. Lastly, the book explores the integration of deep learning with natural language processing techniques, enabling systems to understand, generate, and interpret textual information in multimedia contexts. Throughout the book, practical examples, code snippets, and real-world case studies are provided to help readers gain hands-on experience in implementing deep learning solutions for multimedia processing. Deep Learning for Multimedia Processing is an essential resource for anyone interested in harnessing the power of deep learning to unlock the vast potential of multimedia data"--
Deep Learning For Image Processing Applications
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Author : D.J. Hemanth
language : en
Publisher: IOS Press
Release Date : 2017-12
Deep Learning For Image Processing Applications written by D.J. Hemanth and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12 with Computers categories.
Deep learning and image processing are two areas of great interest to academics and industry professionals alike. The areas of application of these two disciplines range widely, encompassing fields such as medicine, robotics, and security and surveillance. The aim of this book, ‘Deep Learning for Image Processing Applications’, is to offer concepts from these two areas in the same platform, and the book brings together the shared ideas of professionals from academia and research about problems and solutions relating to the multifaceted aspects of the two disciplines. The first chapter provides an introduction to deep learning, and serves as the basis for much of what follows in the subsequent chapters, which cover subjects including: the application of deep neural networks for image classification; hand gesture recognition in robotics; deep learning techniques for image retrieval; disease detection using deep learning techniques; and the comparative analysis of deep data and big data. The book will be of interest to all those whose work involves the use of deep learning and image processing techniques.
Machine Learning Techniques For Multimedia
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Author : Matthieu Cord
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-02-07
Machine Learning Techniques For Multimedia written by Matthieu Cord and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-02-07 with Computers categories.
Processing multimedia content has emerged as a key area for the application of machine learning techniques, where the objectives are to provide insight into the domain from which the data is drawn, and to organize that data and improve the performance of the processes manipulating it. Applying machine learning techniques to multimedia content involves special considerations – the data is typically of very high dimension, and the normal distinction between supervised and unsupervised techniques does not always apply. This book provides a comprehensive coverage of the most important machine learning techniques used and their application in this domain. Arising from the EU MUSCLE network, a program that drew together multidisciplinary teams with expertise in machine learning, pattern recognition, artificial intelligence, and image, video, text and crossmedia processing, the book first introduces the machine learning principles and techniques that are applied in multimedia data processing and analysis. The second part focuses on multimedia data processing applications, with chapters examining specific machine learning issues in domains such as image retrieval, biometrics, semantic labelling, mobile devices, and mining in text and music. This book will be suitable for practitioners, researchers and students engaged with machine learning in multimedia applications.
Deep Learning Based Applications For Multimedia Processing Applications
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Author : Uzair Aslam Bhatti
language : en
Publisher: CRC Press
Release Date : 2024-02
Deep Learning Based Applications For Multimedia Processing Applications written by Uzair Aslam Bhatti and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02 with Computers categories.
Divided into two volumes, Volume 1 begins by introducing the fundamental concepts of deep learning, providing readers with a solid foundation to understand its relevance in multimedia processing. Volumes 2 delves into advanced topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Deep Learning For Multimedia Forensics
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Author : Irene Amerini
language : en
Publisher:
Release Date : 2021-08-31
Deep Learning For Multimedia Forensics written by Irene Amerini and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-31 with Computers categories.
In this survey, the latest trends and deep-learning-based techniques for multimedia forensics are introduced, in both architectural and data-processing. The publication is intended for researchers, students and professionals active in the fields of Deep Learning and Multimedia Forensics.
Deep Learning In Medical Signal And Image Processing
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Author : Aamir, Muhammad
language : en
Publisher: IGI Global
Release Date : 2025-05-23
Deep Learning In Medical Signal And Image Processing written by Aamir, Muhammad and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-23 with Computers categories.
Deep learning is revolutionizing the analysis of medical signals and images, offering unprecedented advancements in diagnostic accuracy and efficiency. Techniques such as convolutional and recurrent neural networks are transforming the processing of radiological scans, ultrasound images, and ECG readings. By enabling more detailed and precise interpretations, deep learning enhances the ability of healthcare providers to make timely and informed decisions. These innovations are reshaping medical workflows, improving patient outcomes, and paving the way for a future of more reliable and efficient healthcare solutions. Deep Learning in Medical Signal and Image Processing offers a comprehensive examination of deep learning, specifically through convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to medical data. It explores the application of AI in the analysis of medical signals and images. Covering topics such as diagnostic accuracy, enhanced decision-making, and data augmentation techniques, this book is an excellent resource for medical practitioners, clinicians, data scientists, AI researchers, healthcare professionals, engineers, professionals, researchers, scholars, academicians, and more.
Deep Learning For Earth Observation And Climate Monitoring
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Author : Uzair Aslam Bhatti
language : en
Publisher: Elsevier
Release Date : 2025-03-19
Deep Learning For Earth Observation And Climate Monitoring written by Uzair Aslam Bhatti and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-19 with Science categories.
Deep Learning for Earth Observation and Climate Monitoring bridges the gap between deep learning and the Earth sciences, offering cutting-edge techniques and applications that are transforming our understanding of the environment. With a focus on practical scenarios, this book introduces readers to the fundamental concepts of deep learning, from classification and image segmentation to anomaly detection and domain adaptability. The book includes practical discussion on regression, parameter retrieval, forecasting, and interpolation, among other topics. With a solid foundational theory, real-world examples, and example codes, it provides a full understanding of how intelligent systems can be applied to enhance Earth observation and especially climate monitoring.This book allows readers to apply learning representations, unsupervised deep learning, and physics-aware models to Earth observation data, enabling them to leverage the power of deep learning to fully utilize the wealth of environmental data from satellite technologies. - Introduces deep learning for classification, covering recent improvements in image segmentation and encoding priors, anomaly detection and target recognition, and domain adaptability - Includes both learning representations and unsupervised deep learning, covering deep learning picture fusion, regression, parameter retrieval, forecasting, and interpolation from a practical standpoint - Provides a number of physics-aware deep learning models, including the code and the parameterization of models on a companion website, as well as links to relevant data repositories, allowing readers to test techniques themselves
Modern Intelligent Techniques For Image Processing
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Author : Bhatti, Uzair Aslam
language : en
Publisher: IGI Global
Release Date : 2025-04-29
Modern Intelligent Techniques For Image Processing written by Bhatti, Uzair Aslam and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-29 with Computers categories.
Modern intelligent techniques, such as deep learning, neural networks, and computer vision algorithms, enable systems to automatically detect patterns, classify objects, and generate high-quality images. With the ability to process vast amounts of visual data, intelligent image processing transforms industries in healthcare, where it aids in techniques like medical imaging analysis or autonomous driving. It ensures real-time object recognition and navigation. Further research into image processing may reveal what these machines can understand and create, making it more efficient, accurate, and versatile. Modern Intelligent Techniques for Image Processing explores modern intelligent techniques for image processing, offering both theoretical foundations and hands-on applications. It examines the way images are analyzed, interpreted, and utilized across various domains including healthcare, autonomous vehicles, security, and entertainment. This book covers topics such as biometrics, image segmentation, and data annotation, and is a useful resource for computer engineers, medical and healthcare professionals, data scientists, academicians, and researchers.
Enhancing Steganography Through Deep Learning Approaches
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Author : Kumar, Vijay
language : en
Publisher: IGI Global
Release Date : 2024-11-04
Enhancing Steganography Through Deep Learning Approaches written by Kumar, Vijay and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-04 with Computers categories.
In an era defined by digital connectivity, securing sensitive information against cyber threats is a pressing concern. As digital transmission systems advance, so do the methods of intrusion and data theft. Traditional security measures often need to catch up in safeguarding against sophisticated cyber-attacks. This book presents a timely solution by integrating steganography, the ancient art of concealing information, with cutting-edge deep learning techniques. By blending these two technologies, the book offers a comprehensive approach to fortifying the security of digital communication channels. Enhancing Steganography Through Deep Learning Approaches addresses critical issues in national information security, business and personal privacy, property security, counterterrorism, and internet security. It thoroughly explores steganography's application in bolstering security across various domains. Readers will gain insights into the fusion of deep learning and steganography for advanced encryption and data protection, along with innovative steganographic techniques for securing physical and intellectual property. The book also delves into real-world examples of thwarting malicious activities using deep learning-enhanced steganography. This book is tailored for academics and researchers in Artificial Intelligence, postgraduate students seeking in-depth knowledge in AI and deep learning, smart computing practitioners, data analysis professionals, and security sector professionals.