CHF114.00
Download est disponible immédiatement
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.
Auteur
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.
Résumé
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:
Contenu
Preface.
Introduction.
Basic Statistical Concepts and Conventional Smoothing
Techniques.
Estimation of Jump Regression Curves.
Estimation of Jump Location Curves of Regression
Surfaces.
Jump Preserving Surface Estimation By Local Smoothing.
Edge Detection In Image Processing.
Edge-Preserving Image Restoration.
References.
Index.