Image Processing and Jump Regression Analysis
Seiten
2005
Wiley-Interscience (Verlag)
978-0-471-42099-6 (ISBN)
Wiley-Interscience (Verlag)
978-0-471-42099-6 (ISBN)
Addresses connections and differences between jump regression analysis and image processing, with the goal of establishing better communication among research groups in the two areas of statistics. This book discusses procedures that are easy to use, simple to compute, and have proven statistical theory behind them.
The first text to bridge the gap between image processing and jump regression analysis Recent statistical tools developed to estimate jump curves and surfaces have broad applications, specifically in the area of image processing. Often, significant differences in technical terminologies make communication between the disciplines of image processing and jump regression analysis difficult. In easy-to-understand language, Image Processing and Jump Regression Analysis builds a bridge between the worlds of computer graphics and statistics by addressing both the connections and the differences between these two disciplines. The author provides a systematic analysis of the methodology behind nonparametric jump regression analysis by outlining procedures that are easy to use, simple to compute, and have proven statistical theory behind them.
Key topics include:
Conventional smoothing procedures
Estimation of jump regression curves
Estimation of jump location curves of regression surfaces
Jump-preserving surface reconstruction based on local smoothing
Edge detection in image processing
Edge-preserving image restoration
With mathematical proofs kept to a minimum, this book is uniquely accessible to a broad readership. It may be used as a primary text in nonparametric regression analysis and image processing as well as a reference guide for academicians and industry professionals focused on image processing or curve/surface estimation.
The first text to bridge the gap between image processing and jump regression analysis Recent statistical tools developed to estimate jump curves and surfaces have broad applications, specifically in the area of image processing. Often, significant differences in technical terminologies make communication between the disciplines of image processing and jump regression analysis difficult. In easy-to-understand language, Image Processing and Jump Regression Analysis builds a bridge between the worlds of computer graphics and statistics by addressing both the connections and the differences between these two disciplines. The author provides a systematic analysis of the methodology behind nonparametric jump regression analysis by outlining procedures that are easy to use, simple to compute, and have proven statistical theory behind them.
Key topics include:
Conventional smoothing procedures
Estimation of jump regression curves
Estimation of jump location curves of regression surfaces
Jump-preserving surface reconstruction based on local smoothing
Edge detection in image processing
Edge-preserving image restoration
With mathematical proofs kept to a minimum, this book is uniquely accessible to a broad readership. It may be used as a primary text in nonparametric regression analysis and image processing as well as a reference guide for academicians and industry professionals focused on image processing or curve/surface estimation.
PEIHUA QIU, PHD, is Associate Professor of Statistics at the University of Minnesota. He has published over twenty-five papers in refereed journals as well as two book chapters. He received his PhD in Statistics at the University of Wisconsin-Madison in 1996.
Preface. 1. Introduction.
2. Basic Statistical Concepts and Conventional Smoothing Techniques.
3. Estimation of Jump Regression Curves.
4. Estimation of Jump Location Curves of Regression Surfaces.
5. Jump Preserving Surface Estimation By Local Smoothing.
6. Edge Detection In Image Processing.
7. Edge-Preserving Image Restoration.
References.
Index.
| Erscheint lt. Verlag | 18.2.2005 |
|---|---|
| Reihe/Serie | Wiley Series in Probability and Statistics |
| Zusatzinfo | Photos: 12 B&W, 0 Color; Graphs: 61 B&W, 0 Color |
| Sprache | englisch |
| Maße | 162 x 240 mm |
| Gewicht | 612 g |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik |
| Technik ► Elektrotechnik / Energietechnik | |
| ISBN-10 | 0-471-42099-9 / 0471420999 |
| ISBN-13 | 978-0-471-42099-6 / 9780471420996 |
| Zustand | Neuware |
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