| 1. |
- Strandmark, Petter
(författare)
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Early Vision Optimization: Parametric Models, Parallelization and Curvature
- 2010
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Early vision is the process occurring before any semantic interpretation of an image takes place. Motion estimation, object segmentation and detection are all parts of early vision, but recognition is not. Many of these tasks are formulated as optimization problems and one of the key factors for the success of recent methods is that they seek to compute globally optimal solutions. This thesis is concerned with improving the efficiency and extending the applicability of the current state of the art. This is achieved by introducing new methods of computing solutions to image segmentation and other problems of early vision. The first part studies parametric problems where model parameters are estimated in addition to an image segmentation. For a small number of parameters these problems can still be solved optimally. In the second part the focus is shifted toward curvature regularization, i.e. when the commonly used length and area regularization is replaced by curvature in two and three dimensions. These problems can be discretized over a mesh and special attention is given to the mesh geometry. Specifically, hexagonal meshes are compared to square ones and a method for generating adaptive methods is introduced and evaluated. The framework is then extended to curvature regularization of surfaces. Thirdly, fast methods for finding minimal graph cuts and solving related problems on modern parallel hardware are developed and extensively evaluated. Finally, the thesis is concluded with two applications to early vision problems: heart segmentation and image registration.
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| 2. |
- Strandmark, Petter, et al.
(författare)
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Parallel and Distributed Graph Cuts by Dual Decomposition
- 2010
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Ingår i: IEEE Conference on Computer Vision and Pattern Recognition. - 1063-6919. - 9781424469840 ; , s. 2085-2092
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Konferensbidrag (refereegranskat)abstract
- Graph cuts methods are at the core of many state-of-the-art algorithms in computer vision due to their efficiency in computing globally optimal solutions. In this paper, we solve the maximum flow/minimum cut problem in parallel by splitting the graph into multiple parts and hence, further increase the computational efficacy of graph cuts. Optimality of the solution is guaranteed by dual decomposition, or more specifically, the solutions to the subproblems are constrained to be equal on the overlap with dual variables. We demonstrate that our approach both allows (i) faster processing on multi-core computers and (ii) the capability to handle larger problems by splitting the graph across multiple computers on a distributed network. Even though our approach does not give a theoretical guarantee of speed-up, an extensive empirical evaluation on several applications with many different data sets consistently shows good performance. An open source C++ implementation of the dual decomposition method is also made publicly available.
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| 3. |
- Svärm, Linus, et al.
(författare)
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Shift-map Image Registration
- 2010
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Konferensbidrag (refereegranskat)abstract
- Shift-map image processing is a new framework based on energy minimization over a large space of labels. The optimization utilizes -expansion moves and iterative refinement over a Gaussian pyramid. In this paper we extend the range of applications to image registration. To do this, new data and smoothness terms have to be constructed. We note a great improvement when we measure pixel similarities with the dense DAISY descriptor. The main contributions of this paper are: • The extension of the shift-map framework to include image registration. We register images for which SIFT only provides 3 correct matches. • A publicly available implementation of shift-map image processing (e.g. inpainting, registration). We conclude by comparing shift-map registration to a recent method for optical flow with favorable results.
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