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.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
"It has much to offer that is hard to find elsewhere." ( Journal of the American Statistical Association , December 2006)
"...a well-written book offering comprehensive discussions...an excellent reference and source book for statisticians, computer scientists, engineers, and other researchers..." ( IIE Transactions- Quality and Reliability Engineering , June 2006)
"...an impressive resource for research statisticians...researchers in computer graphics and image processing..." ( Technometrics , May 2006)
"...a well-written book offering comprehensive discussions...an excellent reference and source book for statisticians, computer scientists, engineers, and other researchers..." ( IIE Transactions- Quality and Reliability Engineering , June 2006)
"...an impressive resource for research statisticians...researchers in computer graphics and image processing..." ( Technometrics , May 2006)