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PUBLICATIONS

  Local, Semi-global, and Global Optimization for Motion Estimation paper              
  Werner Trobin       Phd Thesis 2009  
paper Motion cues are an integral part of our visual experience, and therefore it is not surprising that the recovery of motion information from image sequences is a prominent problem in computer vision. Such motion estimates can, e.g., be obtained using non-parametric variational techniques, but while these techniques yield accurate results on a diverse range of image sequences, there are still a number of open problems. In this thesis, we address two of those open problems: (i) the common practice of regularizing the flow gradient induces a bias towards fronto-parallel flows (a.k.a. staircasing), which is particularly pronounced when using robust penalty functions like the Total Variation, and (ii) variational models are typically minimized by applying local optimization schemes, which are prone to get stuck in local minima. To address problem (i), we introduce a robust regularization approach based on decorrelated second-order derivatives, derive an efficient numerical solution scheme, and demonstrate that this regularizer does not induce staircasing artifacts. We also propose an optimization strategy that facilitates large moves in the solution space of variational models by constructing and solving a series of auxiliary binary problems, thereby outlining one potential solution for problem (ii). Furthermore, we develop a global flow estimation technique that accommodates any positive concave, monotonic regularizer, e.g. truncated Total Variation or generalized Laplacian, and almost arbitrary data terms. Despite this flexibility, the resulting optimization problem remains convex and therefore its globally optimal solution can be computed in polynomial time. We conclude with an extensive evaluation on the challenging Middlebury optical flow data sets, demonstrating the viability of the proposed solutions. In spite of our focus on motion estimation, the presented second-order regularizer as well as the optimization strategies are applicable to other problems in computer vision, e.g. denoising, inpainting, and other correspondence problems.
 
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  Flowball 2009 - An Interactive Game based on Optical Flow paper   videos          
  Wolfgang Paier       Bachelor Thesis  
paper This document roughly covers the steps which are needed to provide a user interface solely based on optical flow. We used two di fferent digital cameras: A simple webcam or an expensive AVT-Marlin industrial camera. We also used two different algorithms to calculate the optical flow...
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  Semi Automatic Segmentation of Articular Cartilage using Variational Methods paper              
  Christian Reinbacher       Master's Thesis  
paper In this Master's Thesis we propose an interactive segmentation framework for the semi automatic segmentation of articular cartilage. Until today, no automatic segmentation method is able achieve the accuracy, necessary for a trustworthy diagnosis. Also, physicians in general prefer to be able to control and modify the segmentation result, which is usually complicated using automatic methods...
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  Globally Optimal TV-L1 Shape Prior Segmentation paper   videos          
  Manuel Werlberger       Master Thesis 2008  
paper Interpreting an image is a common and challenging task in computer vision. A human observer does not only use intensity or color information or other basic features when looking for region boundaries but also takes prior knowledge into account...
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  Fast Total Variation for Computer Vision paper              
  Thomas Pock       Phd Thesis 2008  
paper Motivated by statistical inference methods, variational methods are among the most successful methods to solve a number of different Computer Vision problems. Variational methods aim to minimize an energy functional...
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