Advanced Image Analysis and Recognition (Paperback)

Laxmi Narayan Soni

ISBN 13: 9798897246014

Verlag: Notion Press, 2025

Beschreibung

Paperback. In an era where artificial intelligence and computational imaging are transforming industries, Stochastic Processes and Pattern Recognition in Image Processing serves as a comprehensive guide to mastering probabilistic models, image segmentation, and pattern recognition techniques.This book explores the intersection of stochastic processes and computer vision, bridging fundamental mathematical theories with real-world applications. Covering topics such as Markov random fields, Bayesian inference, probabilistic deep learning, and graph-based segmentation, this book is designed to provide both students and professionals with the knowledge and tools necessary to build robust image processing algorithms.Whether you're an academic researcher, a machine learning engineer, or an AI enthusiast, this book offers: In-depth explanations of stochastic models used in image analysis Step-by-step mathematical formulations and their practical implementations Real-world applications in medical imaging, autonomous systems, and remote sensing Hands-on techniques for enhancing object detection, segmentation, and classificationWith a structured approach, practical examples, and advanced methodologies, this book is an indispensable resource for anyone looking to explore the power of probabilistic reasoning in image processing. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Bestandsnummer des Verkäufers 9798897246014

Bibliografische Details

• Titel: Advanced Image Analysis and Recognition (Paperback)
• Verlag: Notion Press
• Veröffentlichungsdatum: 2025
• Sprache: Englisch
• ISBN-13: 9798897246014
• Bindung: Paperback
• Zustand: new

Über diesen Titel

Inhaltsangabe

In an era where artificial intelligence and computational imaging are transforming industries, Stochastic Processes and Pattern Recognition in Image Processing serves as a comprehensive guide to mastering probabilistic models, image segmentation, and pattern recognition techniques.

This book explores the intersection of stochastic processes and computer vision, bridging fundamental mathematical theories with real-world applications. Covering topics such as Markov random fields, Bayesian inference, probabilistic deep learning, and graph-based segmentation, this book is designed to provide both students and professionals with the knowledge and tools necessary to build robust image processing algorithms.

Whether you're an academic researcher, a machine learning engineer, or an AI enthusiast, this book offers:
✔ In-depth explanations of stochastic models used in image analysis
✔ Step-by-step mathematical formulations and their practical implementations
✔ Real-world applications in medical imaging, autonomous systems, and remote sensing
✔ Hands-on techniques for enhancing object detection, segmentation, and classification

With a structured approach, practical examples, and advanced methodologies, this book is an indispensable resource for anyone looking to explore the power of probabilistic reasoning in image processing.