http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Modern Intelligent Techniques for Image Processing
"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"--Amazon.com
Title Page -- Copyright Page -- Table of Contents -- Detailed Table of Contents -- Preface -- Chapter 1: Deep Learning in Predictive Image Analysis and Forecasting -- Chapter 2: Deep Learning Techniques for Image Clustering and Classification -- Chapter 3: Deep Learning Techniques for Image Segmentation and Data Annotation -- Chapter 4: Deep Learning Techniques for Medical Image Segmentation -- Chapter 5: Explainable AI in Healthcare Imaging for Medical Diagnosis -- Chapter 6: From Pixels to Precision -- Chapter 7: Innovative Deep Learning Image Technologies
Chapter 8: Innovative Techniques for Image Clustering and Classification -- Chapter 9: Lung Diseases Classification Using Artificial Neural Network With Ant Colony Optimization -- Chapter 10: Next-Generation Image Enhancement and Restoration With Artificial Intelligence -- Chapter 11: Online Biometrics -- Chapter 12: Revolutionizing Dentistry -- Chapter 13: Semantic Image Segmentation Employing U-Net-Based Ensemble Model -- Chapter 14: Transformative Deep Learning Approaches for Enhanced Image Analysis and Processing
Chapter 15: A Comparative Analysis of Breast Cancer Classification Using Ensemble and Multi-Classifier Algorithms -- Chapter 16: A Comparative Analysis of Deep Learning Approaches for Unmasking Image Forgeries -- Chapter 17: A Novel Hybrid Wavelet-GAN Image Denoising System -- Chapter 18: Action Recognition Identification Using Deep Learning -- Chapter 19: AI-Driven-IoT (AIIoT)-Based Jawar Leaf Disease Detection -- Chapter 20: Enhancing Brain Tumor Detection and Diagnosis -- Chapter 21: Ethical Considerations in AI-Driven Image Processing
Chapter 22: Revolutionizing Medical Imaging and Diagnostics Through Intelligent Image Processing Techniques -- Compilation of References -- About the Contributors -- Index
[edited by] Uzair Aslam Bhatti [and 3 others].