LEADER 01307nam--2200397---450- 001 990003380770203316 005 20100318142619.0 010 $a88-14-14507-5 035 $a000338077 035 $aUSA01000338077 035 $a(ALEPH)000338077USA01 035 $a000338077 100 $a20100318d2009----km-y0itay50------ba 101 $aita 102 $aIT 105 $a||||||||001yy 200 1 $aStoria del diritto penale e della giustizia$escritti editi e inediti (1972-2007)$fMario Sbriccoli 210 $aMilano$cGiuffrè$d2009 215 $a2 v. (XV, 1338 p. compl.)$d23 cm 225 2 $aPer la storia del pensiero giuridico moderno$v88 410 0$12001$aPer la storia del pensiero giuridico moderno$v, 88 454 1$12001 461 1$1001-------$12001 606 0 $aDiritto penale$2BNCF 676 $a345.45 700 1$aSBRICCOLI,$bMario$0140433 801 0$aIT$bsalbc$gISBD 912 $a990003380770203316 951 $aXXX.A. Coll. 92/ 65 2$b66527 G.$cXXX.A. Coll. 92/$d00256558 951 $aXXX.A. Coll. 92/ 65 1$b66526 G.$cXXX.A. Coll. 92/$d00256557 959 $aBK 969 $aGIU 979 $aFIORELLA$b90$c20100318$lUSA01$h1420 979 $aFIORELLA$b90$c20100318$lUSA01$h1426 996 $aStoria del diritto penale e della giustizia$9224730 997 $aUNISA LEADER 10054nam 2200721 a 450 001 9911004813103321 005 20200520144314.0 010 $a9781615837359 010 $a1615837353 010 $a9780819478450 010 $a0819478458 024 7 $a10.1117/3.268105 035 $a(CKB)2470000000002908 035 $a(EBL)728495 035 $a(OCoLC)631155257 035 $a(SSID)ssj0000386845 035 $a(PQKBManifestationID)11232054 035 $a(PQKBTitleCode)TC0000386845 035 $a(PQKBWorkID)10401131 035 $a(PQKB)10859262 035 $a(MiAaPQ)EBC728495 035 $a(CaBNVSL)gtp00535595 035 $a(SPIE)9780819478450 035 $a(PPN)237382490 035 $a(Perlego)2605761 035 $a(EXLCZ)992470000000002908 100 $a19971006d1999 uy 0 101 0 $aeng 135 $aurbn||||m|||a 181 $ctxt 182 $cc 183 $acr 200 10$aRandom processes for image and signal processing /$fEdward R. Dougherty 210 $aBellingham, Wash. $cSPIE Optical Engineering Press ;$aNew York $cInstitute of Electrical and Electronics Engineers$dc1999 215 $a1 online resource (611 p.) 225 1 $aSPI/IEEE series on imaging science & engineering 300 $aDescription based upon print version of record. 311 08$a9780819425133 311 08$a0819425133 320 $aIncludes bibliographical references and index. 327 $aChapter 1. Probability theory -- Probability space -- Events -- Conditional probability -- Random variables -- Probability distributions -- Probability densities -- Functions of a random variable -- Moments -- Expectation and variance -- Moment-generating function -- Important probability distributions -- Binomial distribution -- Poisson distribution -- Normal distribution -- Gamma distribution -- Beta distribution -- Computer simulation -- Multivariate distributions -- Jointly distributed random variables -- Conditioning -- Independence -- Functions of several random variables -- Basic arithmetic functions of two random variables -- Distributions of sums of independent random variables -- Joint distributions of output random variables -- Expectation of a function of several random variables -- Covariance -- Multivariate normal distribution -- Laws of large numbers -- Weak law of large numbers -- Strong law of large numbers -- Central limit theorem -- Parametric estimation via random samples -- Random-sample estimators -- Sample mean and sample variance -- Minimum-variance unbiased estimators -- Method of moments -- Order statistics -- Maximum-likelihood estimation -- Maximum-likelihood estimators -- Additive noise -- Minimum noise -- Entropy -- Uncertainty -- Information -- Entropy of a random vector -- Source coding -- Prefix codes -- Optimal coding -- Exercises for chapter 1. 327 $aChapter 2. Random processes -- Random functions -- Moments of a random function -- Mean and covariance functions -- Mean and covariance of a sum -- Differentiation -- Differentiation of random functions -- Mean-square differentiability -- Integration -- Mean ergodicity -- Poisson process -- One-dimensional Poisson model -- Derivative of the Poisson process -- Properties of Poisson points -- Axiomatic formulation of the Poisson process -- Wiener process and white noise -- White noise -- Random walk -- Wiener process -- Stationarity -- Wide-sense stationarity -- Mean-ergodicity for WS stationary processes -- Covariance-ergodicity for WS stationary processes -- Strict-sense stationarity -- Estimation -- Linear systems -- Communication of a linear operator with expectation -- Representation of linear operators -- Output covariance -- Exercises for chapter 2. 327 $aChapter 3. Canonical representation -- Canonical expansions -- Fourier representation and projections -- Expansion of the covariance function -- Karhunen-Loeve expansion -- The Karhunen-Loeve theorem -- Discrete Karhunen-Loeve expansion -- Canonical expansions with orthonormal coordinate functions -- Relation to data compression -- Noncanonical representation -- Generalized Bessel inequality -- Decorrelation -- Trigonometric representation -- Trigonometric Fourier series -- Generalized Fourier coefficients for WS stationary processes -- Mean-square periodic WS stationary processes -- Expansions as transforms -- Orthonormal transforms of random functions -- Fourier descriptors -- Transform coding -- Karhunen-Loeve compression -- Transform compression using arbitrary orthonormal systems -- Walsh-Hadamard transform -- Discrete cosine transform -- Transform coding for digital images -- Optimality of the Karhunen-Loeve transform -- Coefficients generated by linear functionals -- Coefficients from integral functionals -- Generating bi-orthogonal function systems -- Complete function systems -- Canonical expansion of the covariance function -- Canonical expansions from covariance expansions -- Constructing canonical expansions for covariance functions -- Integral canonical expansions -- Construction via integral functional coefficients -- Construction from a covariance expansion -- Power spectral density -- The power-spectral-density/autocorrelation transform pair -- Power spectral density and linear operators -- Integral representation of WS stationary random functions -- Canonical representation of vector random functions -- Vector random functions -- Canonical expansions for vector random functions -- Finite sets of random vectors -- Canonical representation over a discrete set -- Exercises for chapter 3. 327 $aChapter 4. Optimal filtering -- Optimal mean-square-error filters -- Conditional expectation -- Optimal nonlinear filter -- Optimal filter for jointly normal random variables -- Multiple observation variables -- Bayesian parametric estimation -- Optimal finite-observation linear filters -- Linear filters and the orthogonality principle -- Design of the optimal linear filter -- Optimal linear filter in the jointly Gaussian case -- Role of wide-sense stationarity -- Signal-plus-noise model -- Edge detection -- Steepest descent -- Steepest descent iterative algorithm -- Convergence of the steepest-descent algorithm -- Least-mean-square adaptive algorithm -- Convergence of the LMS algorithm -- Nonstationary processes -- Least-squares estimation -- Pseudoinverse estimator -- Least-squares estimation for nonwhite noise -- Multiple linear regression -- Least-squares image restoration -- Optimal linear estimation of random vectors -- Optimal linear filter for linearly dependent observations -- Optimal estimation of random vectors -- Optimal linear filters for random vectors -- Recursive linear filters -- Recursive generation of direct sums -- Static recursive optimal linear filtering -- Dynamic recursive optimal linear filtering -- Optimal infinite-observation linear filters -- Wiener-Hopf equation -- Wiener filter -- Optimal linear filter in the context of a linear model -- The linear signal model -- Procedure for finding the optimal linear filter -- Additive white noise -- Discrete domains -- Optimal linear filters via canonical expansions -- Integral decomposition into white noise -- Integral equations involving the autocorrelation function -- Solution via discrete canonical expansions -- Optimal binary filters -- Binary conditional expectation -- Boolean functions and optimal translation-invariant filters -- Optimal increasing filters -- Pattern classification -- Optimal classifiers -- Gaussian maximum-likelihood classification -- Linear discriminants -- Neural networks -- Two-layer neural networks -- Steepest descent for nonquadratic error surfaces -- Sum-of-squares error -- Error back-propagation -- Error back-propagation for multiple outputs -- Adaptive network design -- Exercises for chapter 4. 327 $aChapter 5. Random models -- Markov chains -- Chapman-Kolmogorov equations -- Transition probability matrix -- Markov processes -- Steady-state distributions for discrete-time Markov chains -- Long-run behavior of a two-state Markov chain -- Classification of states -- Steady-state and stationary distributions -- Long-run behavior of finite Markov chains -- Long-run behavior of Markov chains with infinite state spaces -- Steady-state distributions for continuous-time Markov chains -- Irreducible continuous-time Markov chains -- Birth-death model-queues -- Forward and backward Kolmogorov equations -- Markov random fields -- Neighborhood systems -- Determination by conditional probabilities -- Gibbs distributions -- Random Boolean model -- Germ-grain model -- Vacancy -- Hitting -- Linear boolean model -- Granulometries -- Openings -- Classification by granulometric moments -- Adaptive reconstructive openings -- Random sets -- Hit-or-miss topology -- Convergence and continuity -- Random closed sets -- Capacity functional -- Exercises for chapter 5 -- Bibliography -- Index. 330 $aPart of the SPIE/IEEE Series on Imaging Science and Engineering. This book provides a framework for understanding the ensemble of temporal, spatial, and higher-dimensional processes in science and engineering that vary randomly in observations. Suitable as a text for undergraduate and graduate students with a strong background in probability and as a graduate text in image processing courses. 410 0$aSPIE/IEEE series on imaging science & engineering. 606 $aImage processing$xStatistical methods 606 $aStochastic processes 606 $aSignal processing$xStatistical methods 615 0$aImage processing$xStatistical methods. 615 0$aStochastic processes. 615 0$aSignal processing$xStatistical methods. 676 $a621.382/2/0151923 700 $aDougherty$b Edward R$0731599 712 02$aSociety of Photo-optical Instrumentation Engineers. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911004813103321 996 $aRandom processes for image and signal processing$94388008 997 $aUNINA