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Random processes for image and signal processing /
Part 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 stude...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Bellingham, Wash. : New York :
SPIE Optical Engineering Press ; Institute of Electrical and Electronics Engineers,
©1999.
|
| Colección: | SPIE/IEEE series on imaging science & engineering.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Dougherty, Edward R. | |
| 245 | 1 | 0 | |a Random processes for image and signal processing / |c Edward R. Dougherty. |
| 260 | |a Bellingham, Wash. : |b SPIE Optical Engineering Press ; |a New York : |b Institute of Electrical and Electronics Engineers, |c ©1999. | ||
| 300 | |a 1 online resource (xix, 592 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 | ||
| 490 | 1 | |a SPIE/IEEE series on imaging science & engineering | |
| 504 | |a Includes bibliographical references (pages 575-581) and index. | ||
| 505 | 0 | |a Chapter 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. | |
| 505 | 8 | |a Chapter 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. | |
| 505 | 8 | |a Chapter 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. | |
| 505 | 8 | |a Chapter 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. | |
| 505 | 8 | |a Chapter 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. | |
| 520 | |a Part 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. | ||
| 506 | |3 Use copy |f Restrictions unspecified |2 star |5 MiAaHDL | ||
| 533 | |a Electronic reproduction. |b [Place of publication not identified] : |c HathiTrust Digital Library, |d 2010. |5 MiAaHDL | ||
| 538 | |a Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002. |u http://purl.oclc.org/DLF/benchrepro0212 |5 MiAaHDL | ||
| 583 | 1 | |a digitized |c 2010 |h HathiTrust Digital Library |l committed to preserve |2 pda |5 MiAaHDL | |
| 588 | 0 | |a Print version record. | |
| 590 | |a Knovel |b ACADEMIC - Optics & Photonics | ||
| 650 | 0 | |a Image processing |x Statistical methods. | |
| 650 | 0 | |a Stochastic processes. | |
| 650 | 0 | |a Signal processing |x Statistical methods. | |
| 650 | 2 | |a Stochastic Processes | |
| 650 | 6 | |a Traitement d'images |x Méthodes statistiques. | |
| 650 | 6 | |a Processus stochastiques. | |
| 650 | 6 | |a Traitement du signal |x Méthodes statistiques. | |
| 650 | 7 | |a TECHNOLOGY & ENGINEERING |x Mechanical. |2 bisacsh | |
| 650 | 7 | |a Image processing |x Statistical methods |2 fast | |
| 650 | 7 | |a Signal processing |x Statistical methods |2 fast | |
| 650 | 7 | |a Stochastic processes |2 fast | |
| 650 | 7 | |a Bildverarbeitung |2 gnd | |
| 650 | 7 | |a Signalverarbeitung |2 gnd | |
| 650 | 7 | |a Stochastischer Prozess |2 gnd | |
| 650 | 1 | 7 | |a Stochastische processen. |2 gtt |
| 650 | 1 | 7 | |a Beeldverwerking. |2 gtt |
| 650 | 1 | 7 | |a Signaalverwerking. |2 gtt |
| 650 | 7 | |a Traitement du signal |x Méthodes statistiques. |2 ram | |
| 650 | 7 | |a Traitement d'images |x Méthodes statistiques. |2 ram | |
| 650 | 7 | |a Processus stochastiques. |2 ram | |
| 776 | 0 | 8 | |i Print version: |a Dougherty, Edward R. |t Random processes for image and signal processing. |d Bellingham, Wash. : SPIE Optical Engineering Press ; New York : Institute of Electrical and Electronics Engineers, ©1999 |w (DLC) 97042909 |w (OCoLC)37769926 |
| 830 | 0 | |a SPIE/IEEE series on imaging science & engineering. | |
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