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Englisch
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Beschreibung
A modern treatment focusing on learning and inference, with minimal prerequisites, real-world examples and implementable algorithms.
A modern treatment focusing on learning and inference, with minimal prerequisites, real-world examples and implementable algorithms.
Über den Autor
Dr Simon J. D. Prince is a faculty member in the Department of Computer Science at University College London. He has taught courses on machine vision, image processing and advanced mathematical methods. He has a diverse background in biological and computing sciences and has published papers across the fields of computer vision, biometrics, psychology, physiology, medical imaging, computer graphics and HCI.
Inhaltsverzeichnis
Part I. Probability: 1. Introduction to probability; 2. Common probability distributions; 3. Fitting probability models; 4. The normal distribution; Part II. Machine Learning for Machine Vision: 5. Learning and inference in vision; 6. Modeling complex data densities; 7. Regression models; 8. Classification models; Part III. Connecting Local Models: 9. Graphical models; 10. Models for chains and trees; 11. Models for grids; Part IV. Preprocessing: 12. Image preprocessing and feature extraction; Part V. Models for Geometry: 13. The pinhole camera; 14. Models for transformations; 15. Multiple cameras; Part VI. Models for Vision: 16. Models for style and identity; 17. Temporal models; 18. Models for visual words; Part VII. Appendices: A. Optimization; B. Linear algebra; C. Algorithms.
Details
Erscheinungsjahr: | 2019 |
---|---|
Genre: | Informatik |
Rubrik: | Naturwissenschaften & Technik |
Medium: | Buch |
Inhalt: | Gebunden |
ISBN-13: | 9781107011793 |
ISBN-10: | 1107011795 |
Sprache: | Englisch |
Ausstattung / Beilage: | HC gerader Rücken kaschiert |
Einband: | Gebunden |
Autor: | Prince, Simon J. D. |
Hersteller: | Cambridge University Press |
Maße: | 260 x 183 x 36 mm |
Von/Mit: | Simon J. D. Prince |
Erscheinungsdatum: | 22.03.2019 |
Gewicht: | 1,31 kg |
Über den Autor
Dr Simon J. D. Prince is a faculty member in the Department of Computer Science at University College London. He has taught courses on machine vision, image processing and advanced mathematical methods. He has a diverse background in biological and computing sciences and has published papers across the fields of computer vision, biometrics, psychology, physiology, medical imaging, computer graphics and HCI.
Inhaltsverzeichnis
Part I. Probability: 1. Introduction to probability; 2. Common probability distributions; 3. Fitting probability models; 4. The normal distribution; Part II. Machine Learning for Machine Vision: 5. Learning and inference in vision; 6. Modeling complex data densities; 7. Regression models; 8. Classification models; Part III. Connecting Local Models: 9. Graphical models; 10. Models for chains and trees; 11. Models for grids; Part IV. Preprocessing: 12. Image preprocessing and feature extraction; Part V. Models for Geometry: 13. The pinhole camera; 14. Models for transformations; 15. Multiple cameras; Part VI. Models for Vision: 16. Models for style and identity; 17. Temporal models; 18. Models for visual words; Part VII. Appendices: A. Optimization; B. Linear algebra; C. Algorithms.
Details
Erscheinungsjahr: | 2019 |
---|---|
Genre: | Informatik |
Rubrik: | Naturwissenschaften & Technik |
Medium: | Buch |
Inhalt: | Gebunden |
ISBN-13: | 9781107011793 |
ISBN-10: | 1107011795 |
Sprache: | Englisch |
Ausstattung / Beilage: | HC gerader Rücken kaschiert |
Einband: | Gebunden |
Autor: | Prince, Simon J. D. |
Hersteller: | Cambridge University Press |
Maße: | 260 x 183 x 36 mm |
Von/Mit: | Simon J. D. Prince |
Erscheinungsdatum: | 22.03.2019 |
Gewicht: | 1,31 kg |
Warnhinweis