| Record |
Author  |
Debora Gil; Petia Radeva |
| Title |
A Regularized Curvature Flow Designed for a Selective Shape Restoration |
Type |
Journal Article |
| Year |
2004 |
Publication |
IEEE Transactions on Image Processing |
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| Volume |
13 |
Issue |
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Pages |
1444–1458 |
| Keywords |
Geometric flows, nonlinear filtering, shape recovery. |
| Abstract |
Among all filtering techniques, those based exclu- sively on image level sets (geometric flows) have proven to be the less sensitive to the nature of noise and the most contrast preserving. A common feature to existent curvature flows is that they penalize high curvature, regardless of the curve regularity. This constitutes a major drawback since curvature extreme values are standard descriptors of the contour geometry. We argue that an operator designed with shape recovery purposes should include a term penalizing irregularity in the curvature rather than its magnitude. To this purpose, we present a novel geometric flow that includes a function that measures the degree of local irregularity present in the curve. A main advantage is that it achieves non-trivial steady states representing a smooth model of level curves in a noisy image. Performance of our approach is compared to classical filtering techniques in terms of quality in the restored image/shape and asymptotic behavior. We empirically prove that our approach is the technique that achieves the best compromise between image quality and evolution stabilization. |
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IAM;MILAB |
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no |
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BCNPCL @ bcnpcl @ GiR2004b |
Serial |
491 |
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