Title :
Association of adaptative smoothing and Markovian models for detection of valley bottoms on strongly noisy images [nondestructive testing]
Author :
Azencott, R. ; Chalmond, B. ; Coldefy, F.
Author_Institution :
Univ. de Paris-Sud, Orsay, France
fDate :
30 Aug-3 Sep 1992
Abstract :
The paper is related to a nondestructive control industrial task: the detection of defects in γ radiographic images. The images are very noisy and have a strong luminosity gradient. The authors adopt a Bayes-Markov model in order to estimate the noise, the gradient and the defects. The proposed model is general and can be used in other situations for detecting valley bottoms in noisy images
Keywords :
Bayes methods; Markov processes; decision theory; flaw detection; image processing; radiography; Bayes-Markov model; adaptative smoothing; defects detection; flaw detection; image processing; noise estimation; nondestructive control industrial task; strongly noisy images; valley bottoms; Degradation; Humans; Image reconstruction; Industrial control; Power generation; Radio control; Radiography; Signal to noise ratio; Smoothing methods; Surface reconstruction;
Conference_Titel :
Pattern Recognition, 1992. Vol.III. Conference C: Image, Speech and Signal Analysis, Proceedings., 11th IAPR International Conference on
Conference_Location :
The Hague
Print_ISBN :
0-8186-2920-7
DOI :
10.1109/ICPR.1992.201991
