Computational Learning for Adaptive Computer Vision
Produktdetaljer
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.
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.
1. Introduction
2. Learning as a Discipline
3. Basic Paradigms of Learning
4. Selection of Learning Paradigms and Designing Practical Learning Systems for Vision
5. Learning Applied to Low-Level Vision
6. Learning Applied to Intermediate-Level Vision
7. Learning Applied to High-Level Vision
8. Learning Integrated Multi-Level Vision
9. Learning Applied to Integrating Vision and Action
10. Applications
11. Summary