01045nam--2200373---450-99000282395020331620061006135934.0960-214-158-1000282395USA01000282395(ALEPH)000282395USA0100028239520061006h1997----km-y0itay50------baengGRa---||||001yy<<The>> monuments of the AcropolisMaria BrouskariAthensArchaeological receipts fundcopyr. 1997250 p.ill.27 cmIn testa al front.: Ministry of Culture20012001001-------2001AteneAcropoliMonumenti722.809385MPRUSKARĒ,María S.1736080ITsalbcISBD990002823950203316I T ATE 3403 DBCI TBKDBCDBC9020061006USA011359Monuments of the Acropolis4155842UNISA05572nam 22007814a 450 991082394810332120200520144314.0978661027685197812802768591280276851978047035686904703568639780471733157047173315697804717331640471733164(CKB)1000000000355767(EBL)228498(OCoLC)475936401(SSID)ssj0000176491(PQKBManifestationID)11168703(PQKBTitleCode)TC0000176491(PQKBWorkID)10206512(PQKB)11274657(OCoLC)ocm56753633(MiAaPQ)EBC228498(Au-PeEL)EBL228498(CaPaEBR)ebr10114187(CaONFJC)MIL27685(OCoLC)232157560(Perlego)2762210(EXLCZ)99100000000035576720040930d2005 uy 0engur|n|---|||||txtccrImage processing and jump regression analysis /Peihua Qiu1st ed.Hoboken, N.J. John Wileyc20051 online resource (340 p.)Wiley series in probability and statistics"Wiley-Interscience."9780471420996 0471420999 Includes bibliographical references (p. 281-300) and index.Image Processing and Jump Regression Analysis; Contents; List of Figures; List of Tables; Preface; 1 Introduction; 1.1 Images and image representation; 1.1 A conventional coordinate system for expressing an image in industry.; 1.2 Regression curves and sugaces with jumps; 1.2 A log-transformed C-band, HH-polarization, synthetic aperture radar image of an area near Thetford forest, England.; 1.3 December sea-level pressures observed by a Bombay weather station in India during 1921-1992.; 1.3 Edge detection, image restoration, and jump regression analysis1.4 Statistical process control and some other related topics1.5 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; 2.1 Probability density curve of the standard normal distribution.; 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 techniques2.3.1 Univariate kernel estimators2.3.2 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; 2.2 The Nadaraya-Watson (NW) kernel estimator and the local linear kernel (LK) estimator.; 2.3 Behavior of the Nadaraya-Watson (NW) kernel estimator [plot (a)] and the local linear (LK) kernel estimator [plot (b)] of; 2.4.3 Multivariate local polynomial kernel estimators2.4 Behavior of the Nadaraya- Watson (NW) kernel estimator [plot (a)] and the local linear kernel (LK) estimator [plot (b)] o2.4.4 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; 2.5 Four B-splines when ti, tj+1,tj+2, tj+3, and tj+4 are 0, 0.25, 0.5, 0.75, and 1.0.; 2.6 Wavelet transformation methods; 2.6.1 Function estimation based on Fourier transformation; 2.6.2 Univariate wavelet transformations2.6 The Haar father wavelet, the Haar mother wavelet, the Haar wavelet function y1,0, and the Haar wavelet function y1,1.2.6.3 Bivariate wavelet transformations; Problems; 2.7 When f(x) and y(x) are the Haar father and mother wavelets, the two-dimensional wavelet functions F(x, y), Y(1)(x, y), Y(2)(x, y), and Y(3)(x, y) are displayed.; 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 procedures3.1 The true regression function f and the jump detection criterion MDKE dejined by expression (3.2) when c = 0,n = 100, and hn = 0.1.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 dWiley series in probability and statistics.Image processingRegression analysisImage processing.Regression analysis.006.3/7Qiu Peihua1965-448018MiAaPQMiAaPQMiAaPQBOOK9910823948103321Image processing and jump regression analysis103542UNINA