Statistical Optimization for Geometric Computation - Theory and Practice

Klappentext This text discusses the mathematical foundations of statistical inference for building 3-dimensional models from image and sensor data that contain noise -- a task involving autonomous robots guided by video cameras and sensors. The text employs a theoretical accuracy for the optimization procedure, which maximizes the reliability of estimations based on noise data. 1996 edition. Inhaltsverzeichnis 1. Introduction2. Fundamentals of Linear Algebra3. Probabilities and Statistical Estimation4. Representation of Geometric Objects5. Geometric Correction6. 3-D Computation by Stereo Vision7. Parametric Fitting8. Optimal Filter9. Renormalization10. Applications of Geometric Estimation11. 3-D Motion Analysis12. 3-D Interpretation of Optical Flow13. Information Criterion for Model Selection14. General Theory of Geometric EstimationReferencesIndex

Dettagli sul prodotto

• Autori: Kenichi Kanatani
• Editore: Dover Publications Inc.
• Lingue: Inglese
• Formato: Tascabile
• Pubblicazione: 29.07.2005
• EAN: 9780486443089
• ISBN: 978-0-486-44308-9
• Pagine: 528
• Dimensioni: 140 mm x 222 mm x 32 mm
• Serie: Dover Books on Mathematics; Dover Books on Mathematics
• Categorie: Saggistica > Enciclopedie, opere di consultazione; Scienze naturali, medicina, informatica, tecnica > Informatica, EDP > Informatica