Computational Learning for Adaptive Computer Vision

2018. Auflage 2016
ISBN: 978-0-387-23703-9
Verlag: Springer Us

Buch, Englisch, 505 Seiten, Book, Format (B × H): 152 mm x 229 mm

Computer vision (CV) research seeks to provide computers with human-like perception capabilities so that they can sense the environment, understand the sensed data, take appropriate actions, and learn from this experience in order to enhance future performance. The field has evolved from early pattern recognition and image processing to advanced image understanding, including model-based and knowledge-based vision. This book shows how machine learning can help create robust, flexible vision techniques for optimal functioning in real-world scenarios.

Zielgruppe

Research

Autoren/Hrsg.

Bhanu, Bir

Das, Subhodev

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision

Inhaltsverzeichnis

1. Introduction2. Learning as a Discipline3. Basic Paradigms of Learning4. Selection of Learning Paradigms and Designing Practical Learning Systems for Vision5. Learning Applied to Low-Level Vision6. Learning Applied to Intermediate-Level Vision7. Learning Applied to High-Level Vision8. Learning Integrated Multi-Level Vision9. Learning Applied to Integrating Vision and Action10. Applications11. Summary