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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Qiu, Peihua, 1965-
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Hoboken, N.J. : John Wiley, ©2005.
Colección:Wiley series in probability and statistics.
Temas:
Image processing.
Regression analysis.
Regression Analysis
Traitement d'images.
Analyse de régression.
image processing.
COMPUTERS > Computer Vision & Pattern Recognition.
Image processing
Regression analysis
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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.

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