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Image processing and jump regression analysis /
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 breakdown of the methodology behind nonparametric jump regressio...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, N.J. :
John Wiley,
©2005.
|
| Colección: | Wiley series in probability and statistics.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 037 | |a 110433459 |b Wiley InterScience |n http://www3.interscience.wiley.com | ||
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| 100 | 1 | |a Qiu, Peihua, |d 1965- | |
| 245 | 1 | 0 | |a Image processing and jump regression analysis / |c Peihua Qiu. |
| 260 | |a Hoboken, N.J. : |b John Wiley, |c ©2005. | ||
| 300 | |a 1 online resource (xxiii, 305 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a data file |2 rda | ||
| 490 | 1 | |a Wiley series in probability and statistics | |
| 500 | |a "Wiley-Interscience." | ||
| 504 | |a Includes bibliographical references (pages 281-300) and index. | ||
| 505 | 0 | |a Cover -- Contents -- Preface -- 1 Introduction -- 1.1 Images and image representation -- 1.2 Regression curves and sugaces with jumps -- 1.3 Edge detection, image restoration, and jump regression analysis -- 1.4 Statistical process control and some other related topics -- 1.5 Organization of the book -- Problems -- 2 Basic Statistical Concepts and Conventional Smoothing Techniques -- 2.1 Introduction -- 2.2 Some basic statistical concepts and terminologies -- 2.2.1 Populations, samples, and distributions -- 2.2.2 Point estimation of population parameters -- 2.2.3 Confidence intervals and hypothesis testing -- 2.2.4 Maximum likelihood estimation and least squares estimation -- 2.3 Nadaraya- Watson and other kernel smoothing techniques -- 2.3.1 Univariate kernel estimators -- 2.3.2 Some statistical properties of kernel estimators -- 2.3.3 Multivariate kernel estimators -- 2.4 Local polynomial kernel smoothing techniques -- 2.4.1 Univariate local polynomial kernel estimators -- 2.4.2 Some statistical properties -- 2.4.3 Multivariate local polynomial kernel estimators -- 2.4.4 Bandwidth selection -- 2.5 Spline smoothing procedures -- 2.5.1 Univariate smoothing spline estimation -- 2.5.2 Selection of the smoothing parameter -- 2.5.3 Multivariate smoothing spline estimation -- 2.5.4 Regression spline estimation -- 2.6 Wavelet transformation methods -- 2.6.1 Function estimation based on Fourier transformation -- 2.6.2 Univariate wavelet transformations -- 2.6.3 Bivariate wavelet transformations -- Problems -- 3 Estimation of Jump Regression Curves -- 3.1 Introduction -- 3.2 Jump detection when the number of jumps is known -- 3.2.1 Difference kernel estimation procedures -- 3.2.2 Jump detection based on local linear kernel smoothing -- 3.2.3 Estimation of jump regression functions based on semiparametric modeling -- 3.2.4 Estimation of jump regression functions by spline smoothing -- 3.2.5 Jump and cusp detection by wavelet transformations -- 3.3 Jump estimation when the number of jumps is unknown -- 3.3.1 Jump detection by comparing three local estimators -- 3.3.2 Estimation of the number of jumps by a sequence of hypothesis tests -- 3.3.3 Jump detection by DAKE -- 3.3.4 Jump detection by local polynomial regression -- 3.4 Jump-preserving curve estimation -- 3.4.1 Jump curve estimation by split linear smoothing -- 3.4.2 Jump-preserving curve fitting based on local piecewise-linear kernel estimation -- 3.4.3 Jump-preserving smoothers based on robust estimation -- 3.5 Some discussions -- Problems -- 4 Estimation of Jump Location Curves of Regression Surfaces -- 4.1 Introduction -- 4.2 Jump detection when the number of jump location curves is known -- 4.2.1 Jump detection by RDKE -- 4.2.2 Minimax edge detection -- 4.2.3 Jump estimation based on a contrast statistic -- 4.2.4 Algorithms for tracking the JLCs -- 4.2.5 Estimation of JLCs by wavelet transformations -- 4.3 Detection of arbitrary jumps by local smoothing -- 4.3.1 Treat JLCs as a pointset in the design space -- 4.3.2 Jump detection by local linear estimation -- 4.3.3 Two modijication procedures -- 4.4 Jump detection in two or more given directions -- 4.4.1 Jump detection in two given directions -- 4.4.2 Measuring the p. | |
| 520 | |a 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 breakdown 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. | ||
| 588 | 0 | |a Print version record. | |
| 546 | |a English. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Image processing. | |
| 650 | 0 | |a Regression analysis. | |
| 650 | 2 | |a Regression Analysis | |
| 650 | 6 | |a Traitement d'images. | |
| 650 | 6 | |a Analyse de régression. | |
| 650 | 7 | |a image processing. |2 aat | |
| 650 | 7 | |a COMPUTERS |x Computer Vision & Pattern Recognition. |2 bisacsh | |
| 650 | 7 | |a Image processing |2 fast | |
| 650 | 7 | |a Regression analysis |2 fast | |
| 758 | |i has work: |a Image processing and jump regression analysis (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGPqMvdPfbxv9FK6vFJTDy |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Qiu, Peihua, 1965- |t Image processing and jump regression analysis. |d Hoboken, N.J. : John Wiley, ©2005 |w (DLC) 2004053000 |
| 830 | 0 | |a Wiley series in probability and statistics. | |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=228498 |z Texto completo |
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