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

Volume

46

Issue

6

fYear

1999

fDate

6/1/1999 12:00:00 AM

Firstpage

686

Lastpage

699

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;

fLanguage

English

Journal_Title

Circuits and Systems I: Fundamental Theory and Applications, IEEE Transactions on

Publisher

ieee

ISSN

1057-7122

Type

jour

DOI

10.1109/81.768825

Filename

768825