Augmented Lagrangian method for image segmentation using elastica energy that prefers convex contours
Duration: 45 mins 4 secs
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About this item
| Description: |
Tai, X
Thursday 7th September 2017 - 09:00 to 09:50 |
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| Created: | 2017-09-08 08:50 |
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| Collection: | Variational methods and effective algorithms for imaging and vision |
| Publisher: | Isaac Newton Institute |
| Copyright: | Tai, X |
| Language: | eng (English) |
| Distribution: |
World
(downloadable)
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| Explicit content: | No |
| Aspect Ratio: | 16:9 |
| Screencast: | No |
| Bumper: | UCS Default |
| Trailer: | UCS Default |
| 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 theassociated functional. In this work, we propose using augmented Lagrangianmethod to tackle the minimization problem. Especially, we design a novel augmentedLagrangian functional that deals with the mean curvature term differently as those ones in theprevious 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 methodand also demonstrate new features of this particular segmentation model, such as shapedriven and data driven properties. |
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