Authors:
María E. Buemi
1
;
Juliana Gambini
1
;
Julio C. Jacobo
1
;
Marta E. Mejail
1
and
Alejandro C. Frery
2
Affiliations:
1
Universidad de Buenos Aires, Argentina
;
2
Universidade Federal de Alagoas, Brazil
Keyword(s):
Speckle, contour fitting, B-Spline, anisotropic diffusion, maximum likelihood.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Feature Extraction
;
Features Extraction
;
Image and Video Analysis
;
Informatics in Control, Automation and Robotics
;
Segmentation and Grouping
;
Signal Processing, Sensors, Systems Modeling and Control
;
Statistical Approach
Abstract:
Images obtained with the use of coherent illumination are affected by a noise called speckle, which is inherent to this type of imaging systems. In this work, speckled data have been statistically treated with a multiplicative model using the family of G distributions. One of the parameters of these distributions can be used to characterize the different degrees of roughness found in speckled data. We used this information to find boundaries between different regions within the image. Two different region contour detection methods for speckled imagery, are presented and compared. The first one maximizes a likelihood function over the speckled data and the second one uses anisotropic difussion over roughness estimates. To represent detected contours, the B-Spline curve representation is used. In order to compare the behaviour of the two methods we performed a Monte Carlo experience. It consisted of the generation of a set of test images with a randomly shaped region, which is consider
ed in the literature as a difficult contour to fit. Then, the mean square error was calculated for each test image, for both methods.
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