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Proceedings Paper

Detection of local objects in radiographic images by structural hypothesis-testing approach
Author(s): Roman M. Palenichka; Peter Zinterhof
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Paper Abstract

Detection and binary segmentation of low-contrast flaws (defects) in noisy radiographic images is considered with an application to non-destructive evaluation of materials and industrial articles. The known approaches, like the edge detection or unsharp masking with a consecutive thresholding operation, yield poor results for such images. In the presented method of object detection, a model-based approach is adopted which relies on shape constraints of the objects to be detected as well as exploits the image multiresolution representation. For detection of local objects, the maximum likelihood principle and statistical hypothesis testing is used with the confidence control during all stages of the image analysis. The proposed novel procedure of estimation of the image intensity from noisy pixels ensures a robust evaluation of basic model parameters in the presence of outliers which are considered as impulsive noise.

Paper Details

Date Published: 14 October 1997
PDF: 12 pages
Proc. SPIE 3167, Statistical and Stochastic Methods in Image Processing II, (14 October 1997); doi: 10.1117/12.290276
Show Author Affiliations
Roman M. Palenichka, Institute of Physics and Mechanics (Ukraine)
Peter Zinterhof, Salzburg Univ. (Austria)

Published in SPIE Proceedings Vol. 3167:
Statistical and Stochastic Methods in Image Processing II
Francoise J. Preteux; Jennifer L. Davidson; Edward R. Dougherty, Editor(s)

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