| 1. |
- Kahl, Fredrik, et al.
(författare)
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Generalized Roof Duality for Pseudo-Boolean Optimization
- 2011
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Ingår i: IEEE International Conference on Computer Vision (ICCV). ; , s. 255-262
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Konferensbidrag (refereegranskat)abstract
- The number of applications in computer vision that model higher-order interactions has exploded over the last few years. The standard technique for solving such problems is to reduce the higher-order objective function to a quadratic pseudo-boolean function, and then use roof duality for obtaining a lower bound. Roof duality works by constructing the tightest possible lower-bounding submodular function, and instead of optimizing the original objective function, the relaxation is minimized. We generalize this idea to polynomials of higher degree, where quadratic roof duality appears as a special case. Optimal relaxations are defined to be the ones that give the maximum lower bound. We demonstrate that important properties such as persistency still hold and how the relaxations can be efficiently constructed for general cubic and quartic pseudo-boolean functions. From a practical point of view, we show that our relaxations perform better than state-of-the-art for a wide range of problems, both in terms of lower bounds and in the number of assigned variables.
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| 2. |
- Strandmark, Petter, et al.
(författare)
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Curvature Regularization for Curves and Surfaces in a Global Optimization Framework
- 2011
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Ingår i: Lecture Notes in Computer Science.
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Konferensbidrag (refereegranskat)abstract
- Length and area regularization are commonplace for inverse problems today. It has however turned out to be much more difficult to incorporate a curvature prior. In this paper we propose several improvements to a recently proposed framework based on global optimization. We identify and solve an issue with extraneous arcs in the original formulation by introducing region consistency constraints. The mesh geometry is analyzed both from a theoretical and experimental viewpoint and hexagonal meshes are shown to be superior. We demonstrate that adaptively generated meshes significantly improve the performance. Our final contribution is that we generalize the framework to handle mean curvature regularization for 3D surface completion and segmentation.
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| 3. |
- Strandmark, Petter, et al.
(författare)
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Parallel and Distributed Vision Algorithms Using Dual Decomposition
- 2011
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Ingår i: Computer Vision and Image Understanding. - : Elsevier BV. - 1077-3142. ; 115:12, s. 1721-1732
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Tidskriftsartikel (refereegranskat)abstract
- We investigate dual decomposition approaches for optimization problems arising in low-level vision. Dual decomposition can be used to parallelize existing algorithms, reduce memory requirements and to obtain approximate solutions of hard problems. An extensive set of experiments are performed for a variety of application problems including graph cut segmentation, curvature regularization and more generally the optimization of MRFs. We demonstrate that the technique can be useful for desktop computers, graphical processing units and supercomputer clusters. To facilitate further research, an implementation of the decomposition methods is made publicly available.
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| 4. |
- Ulén, Johannes, et al.
(författare)
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Optimization for Multi-Region Segmentation of Cardiac MRI
- 2011
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Ingår i: Lecture Notes in Computer Science (Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges Second International Workshop, STACOM 2011, Held in Conjunction with MICCAI 2011, Toronto, ON, Canada, September 22, 2011, Revised Selected Papers). - Berlin, Heidelberg : Springer Berlin Heidelberg. - 1611-3349 .- 0302-9743. - 9783642283260 - 9783642283253 ; 7085, s. 129-138
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Konferensbidrag (refereegranskat)abstract
- We introduce a new multi-region model for simultaneous segmentation of the left and right ventricles, myocardium and the left ventricular papillary muscles in MRI. The model enforces geometric constraints such as inclusion and exclusion between the regions, which makes it possible to correctly segment different regions even though the intensity distributions are identical. We efficiently optimize the model using Lagrangian duality which is faster and more memory efficient than current state of the art. As the optimization is based on global techniques, the resulting segmentations are independent of initialization. We evaluate our approach on two benchmarks with competitive results.
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