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Deep Learning for Multimedia Processing Applications: Volume Two: Signal Process
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- ¡Cómpralo ya!Deep Learning for Multimedia Processing Applications: Volume Two: Signal Process
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N.º de artículo de eBay:146247994708
Última actualización el 24 jul 2025 12:01:11 H.EspVer todas las actualizacionesVer todas las actualizaciones
Características del artículo
- Estado
- Book Title
- Deep Learning for Multimedia Processing Applications: Volume T
- ISBN
- 9781032623344
Acerca de este producto
Product Identifiers
Publisher
CRC Press LLC
ISBN-10
1032623349
ISBN-13
9781032623344
eBay Product ID (ePID)
21062640471
Product Key Features
Number of Pages
480 Pages
Language
English
Publication Name
Deep Learning for Multimedia Processing Applications : Volume Two: Signal Processing
Subject
Computer Science, Neural Networks, General, Interactive & Multimedia
Publication Year
2024
Type
Textbook
Subject Area
Computers, Science
Format
Hardcover
Dimensions
Item Length
10 in
Item Width
7 in
Additional Product Features
Intended Audience
College Audience
LCCN
2023-034276
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
006.31
Table Of Content
1. A Review on Comparative Study of Image-Denoising in Medical Imaging Nasir Ishfaq. 2. Remote Sensing Image Classification: A Comprehensive Review and Applications Uzair Aslam Bhatti, Jingbing Li, Saqib Ali Nawaz, Huang Mengxing, and Raza Muhammad Ahmad. 3. Deep learning framework for Face Detection and Recognition for Dark Faces using VGG19 with Variant of Histogram Equalization Kirti and Gagandeep. 4. A 3D Method for combining Geometric Verification and Volume Reconstruction in a Photo Tourism system Muhammad Sajid Khan and Andrew Ware. 5. Deep Learning Algorithms and Architectures for Multimodal Data Analysis Anwar Ali Sathio, Prof. Dr. Muhammad Malook Rind, and Dr. Abdullah Lakhan. 6. Deep Learning Algorithms - Clustering and Classifications for Multimedia Data Anwar Ali Sathio, Prof. Dr. Muhammad Malook Rind, and Dr. Abdullah Lakhan. 7. A Non-Reference Low-Light Image Enhancement Approach using Deep Convolutional Neural Networks Ziaur Rahman, Muhammad Aamir, Kanza Gulzar, Jameel Ahmed Bhutto, Muhammad Ishfaq, Zaheer Ahmed Dayo, and Khalid Hussain Mohammadani. 8. Human Pose Analysis and Gesture Recognition: Methods and Applications Muhammad Haroon, Saud Altaf, Kanza Gulzar, and Muhammad Aamir. 9. Human Action Recognition Using ConvLSTM with Adversarial Noise and Compressive-Sensing-Based Dimensionality Reduction Concise and Informative Mohsin Raza Siyal, Mansoor Ebrahim, Dr.Nadeem Qazi, Syed Hasan Adil, and Kamran Raza. 10. Application of Machine Learning to Urban Ecology Mir Muhammad Nizamani, Ghulam Muhae-Ud-Din, Qian Zhang, Muhammad Awais, Muhammad Qayyum, Muhammad Farhan, Muhammad Jabran, and Yong Wang. 11. Application of Machine Learning in Urban Land Use Haili Zhang and Qin Zhou. 12. Application of GIS and Remote Sensing Technology in Ecosystem Services and Biodiversity Conservation Mir Muhammad Nizamani, Qian Zhang, Ghulam Muhae-Ud-Din, Muhammad Awais, Muhammad Qayyum, Muhammad Farhan, Muhammad Jabran, and Yong Wang. 13. From Data Quality to Model Performance: Navigating the Landscape of Deep Learning Model Evaluation Muhammad Akram, Wajid Hassan Moosa, and Najiba. 14. Deep Learning for the Turnover Intention of Industrial Workers: Evidence from Vietnam Nguyen Ngoc Long, Nguyen Ngoc Lam, and Bui Huy Khoi. 15. Deep Learning for Multimedia Analysis Hafiz Gulfam Ahmad Umar. 16. Challenges and Techniques to Improve Deep Detection and Recognition Methods for Text Spotting Anuj Abraham and Shitala Prasad. 17. Leaf Classification and Disease Detection Based on R-CCN Deep Learning Approach Tayyab Rehman, Muhammad Sajid Khan, and Noshina Tariq. 18. Deep Learning for Multimedia Analysis: Applications, Challenges, and Future Directions Dr. Ahmed Mateen Buttar, Muhammad Anwar Shahid, Muhammad Nouman Arshad, and Irfan Ali.
Synopsis
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, Volumes Two delves into advanced topics such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), explaining their unique capabilities in multimedia tasks. 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., This book is a comprehensive guide that explores the revolutionary impact of deep learning techniques in the field of multimedia processing. Volumes Two delves into advanced topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks, explaining their unique capabilities in multimedia tasks., 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 Two delves into advanced topics such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), explaining their unique capabilities in multimedia tasks. 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.
LC Classification Number
Q325.73.D4 2024
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