A bayesian approach to edge detection in images

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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Bibliographic Details
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
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Summary: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.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.1980.1102229