A bayesian approach to edge detection in images

New statistical techniques for the edge detection problem in images are developed. The image is modeled by signal and noise, which are independent, additive, Gaussian, and autoregressive in two dimensions. The optimal solution, in terms of statistical decision theory, leads to a test that decides am...

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Published in:	IEEE transactions on automatic control Vol. 25; no. 1; pp. 36 - 44
Main Authors:	Mascarenhas, N., Prado, L.
Format:	Journal Article
Language:	English
Published:	IEEE 01-02-1980
Subjects:	Additive noise Bayesian methods Circuit noise Computational modeling Electromagnetic interference Image edge detection Image storage Mathematical model Random variables Testing
Online Access:	Get full text
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Abstract	New statistical techniques for the edge detection problem in images are developed. The image is modeled by signal and noise, which are independent, additive, Gaussian, and autoregressive in two dimensions. The optimal solution, in terms of statistical decision theory, leads to a test that decides among multiple, composite, overlapping hypotheses. A redefinition of the problem, involving nonoverlapping hypotheses, allows the formulation of a computationally attractive scheme. Results are presented with both simulated data and real satellite images. A comparison with standard gradient techniques is made.
AbstractList	New statistical techniques for the edge detection problem in images are developed. The image is modeled by signal and noise, which are independent, additive, Gaussian, and autoregressive in two dimensions. The optimal solution, in terms of statistical decision theory, leads to a test that decides among multiple, composite, overlapping hypotheses. A redefinition of the problem, involving nonoverlapping hypotheses, allows the formulation of a computationally attractive scheme. Results are presented with both simulated data and real satellite images. A comparison with standard gradient techniques is made.
Author	Prado, L. Mascarenhas, N.
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References	abramowitz (ref13) 1965 middleton (ref7) 1965 ref14 ref2 graybill (ref10) 1969 davenport (ref11) 1970 hildebrand (ref12) 1956 ref8 ref9 ref4 ref3 ref6 rosenfeld (ref1) 1976 crane (ref5) 1972; ge 10
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Snippet	New statistical techniques for the edge detection problem in images are developed. The image is modeled by signal and noise, which are independent, additive,...
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SubjectTerms	Additive noise Bayesian methods Circuit noise Computational modeling Electromagnetic interference Image edge detection Image storage Mathematical model Random variables Testing
Title	A bayesian approach to edge detection in images
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