20 March 2001 Characterization of the recognition and the identification capabilities of the statistical snake at low resolution and high noise levels in speckled images
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Abstract
Target recognition is an important task for many automatic systems based on imagery. The recent technique of active contours (snakes) is well adapted to the segmentation step when the recognition is made from the shape of the target. Classical segmentation strategies are generally edge-based in the sense that the segmentation is driven from an edge map of the scene. Consequently, these methods which are efficient with a certain class of problem could fail in presence of strong noise. We have recently proposed an original approach for the statistical segmentation of an object (statistical snake) for which the image is assumed to be made of two regions (the object and the background) composed of homogeneous intensity random fields. In this article, we characterize the quality of the segmentation as a function of the target resolution and noise level with two similarity measurements based on Hausdorff distance between the exact contour and the result of the segmentation.
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Olivier Ruch, Olivier Ruch, Philippe Refregier, Philippe Refregier, } "Characterization of the recognition and the identification capabilities of the statistical snake at low resolution and high noise levels in speckled images", Proc. SPIE 4387, Optical Pattern Recognition XII, (20 March 2001); doi: 10.1117/12.421135; https://doi.org/10.1117/12.421135
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