Multi-Scale Hierarchical Approaches
Buch, Englisch, 162 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 424 g
ISBN: 978-1-4614-5631-5
Verlag: Springer
3D Surface Reconstruction: Multi-Scale Hierarchical Approaches presents methods to model 3D objects in an incremental way so as to capture more finer details at each step. The configuration of the model parameters, the rationale and solutions are described and discussed in detail so the reader has a strong understanding of the methodology. Modeling starts from data captured by 3D digitizers and makes the process even more clear and engaging.
Innovative approaches, based on two popular machine learning paradigms, namely Radial Basis Functions and the Support Vector Machines, are also introduced. These paradigms are innovatively extended to a multi-scale incremental structure, based on a hierarchical scheme. The resulting approaches allow readers to achieve high accuracy with limited computational complexity, and makes the approaches appropriate for online, real-time operation. Applications can be found in any domain in which regression is required.
3D Surface Reconstruction: Multi-Scale Hierarchical Approaches is designed as a secondary text book or reference for advanced-level students and researchers in computer science. This book also targets practitioners working in computer vision or machine learning related fields.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Grafikprogrammierung
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision
- Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
- Technische Wissenschaften Sonstige Technologien | Angewandte Technik Signalverarbeitung, Bildverarbeitung, Scanning
Weitere Infos & Material
Introduction.- Scanner systems.- Reconstruction.- Surface fitting as a regression problem.- Hierarchical Radial Basis Functions Networks.- Hierarchical Support Vector Regression.- Conclusion.
