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 students with a strong background in probability and as a graduate text in image processing courses.
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Contents
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Probability Theory
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1 |
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Exercises for Chapter 1
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107 |
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Random Processes
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115 |
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Copyright
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according to Eq algorithm applied assume autocorrelation binary canonical expansion Chapman-Kolmogorov equations components conditional expectation consider convergence coordinate functions covariance function covariance matrix defined denote derivative deterministic discrete eigenvalues equation error Example finite Fourier coefficients gamma distribution given Hence Huffman code identically distributed image processing inner product inner product space integral interval Kx(t linear estimator linear filter linearly independent Markov chain mean-square moment-generating function normally distributed optimal filter optimal linear filter orthogonal orthonormal system output parameter pixel Poisson points Poisson process possessing probability distribution function pseudoinverse R₁ random function random function X(t random process random variables random vector realization recursive respectively Rx(t Show signal space stationary random subspace Suppose t₁ Theorem transform u₁ uncorrelated values variance o² W₁ white noise X₁ Y₁ yields Z₁