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Euler's elastica based segmentation models and the fast algorithms

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In this talk, we will discuss two image segmentation models that employ L1 and L2 Euler's elastica respectively as the regularization of active contour. When compared with the conventional contour length based regularization, these high order regularizations lead to new features, including connecting broken parts of objects automatically and being well-suited for fine elongate structures. More interestingly, with the L^1 Euler's elastica as the contour regularization, the segmentation model is able to single out objects with convex shapes. We will also discuss the fast algorithms for dealing with these models by using augmented Lagrangian method. Numerical experiments will be presented to illustrate the features of these Euler's elastica based segmentation models.

This talk is part of the Isaac Newton Institute Seminar Series series.

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