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【Academic Report】Augmented Lagrangian method for image segmentation using elastica energy that prefers convex contours
2017-07-14 11:24  

【Topic】:Augmented Lagrangian method for image segmentation using elastica energy that prefers convex contours

【Time】:9:00-10:00 July 18th  

【Place】:C08-201

【Reporter】:Professor Tai Xuecheng

Univerisity of Bergen

Hong Kong Baptist University

【Abstract】:In the talk, we consider an Euler's elastica based image segmentation model. An interesting feature of this model lies in its preference of convex segmentation contour. However, due to the high order and non-differentiable term, it is often nontrivial to minimize the associated functional. In this work, we propose using augmented Lagrangian method to tackle the minimization problem. Especially, we design a novel augmented Lagrangian functional that deals with the mean curvature term differently as those ones in the previous works. The new treatment reduces the number of Lagrange multipliers employed, and more importantly, it helps represent the curvature more effectively and faithfully. Numerical experiments validate the efficiency of the proposed augmented Lagrangian method and also demonstrate new features of this particular segmentation model, such as shape driven and data driven properties.

 

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