Title :
A Bayesian approach to edge detection in noisy images
Author :
De Santis, Alberto ; Sinisgalli, Carmela
Author_Institution :
Dipt. di Inf. e Sistemistica, Rome Univ., Italy
fDate :
6/1/1999 12:00:00 AM
Abstract :
An adaptive method for edge detection in monochromatic unblurred noisy images is proposed. It is based on a linear stochastic signal model derived from a physical image description. The presence of an edge is modeled as a sharp local variation of the gray-level mean value. In any pixel, the statistical model parameters are estimated by means of a Bayesian procedure. Then an hypothesis test, based on the likelihood ratio statistics, is adopted to mark a pixel as an edge point. This technique exploits the estimated local signal characteristics and does not require any overall thresholding procedure
Keywords :
Bayes methods; adaptive estimation; adaptive signal processing; edge detection; noise; nonlinear estimation; stochastic processes; Bayesian procedure; adaptive method; edge detection; estimated local signal characteristics; gray-level mean value; likelihood ratio statistics; linear stochastic signal model; monochromatic unblurred images; noisy images; physical image description; sharp local variation; statistical model parameters estimation; Bayesian methods; Filtering; Geophysics computing; Image edge detection; Image processing; Image segmentation; Signal processing; Stochastic processes; Stochastic resonance; Testing;
Journal_Title :
Circuits and Systems I: Fundamental Theory and Applications, IEEE Transactions on
