Early Vision Optimization: Parametric Models, Parallelization and Curvature

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

Discrete Optimization in Early Vision - Model Tractability Versus Fidelity

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. Some models in early vision are easy to perform inference with---they are tractable. Others describe the reality well---they have high fidelity. This thesis improves the tractability-fidelity trade-off of the current state of the art by introducing new discrete methods for image segmentation and other problems of early vision. The first part studies pseudo-boolean optimization, both from a theoretical perspective as well as a practical one by introducing new algorithms. The main result is the generalization of the roof duality concept to polynomials of higher degree than two. Another focus is parallelization; discrete optimization methods for multi-core processors, computer clusters, and graphical processing units are presented. Remaining in an image segmentation context, the second part studies parametric problems where a set of model parameters and a segmentation are estimated simultaneously. For a small number of parameters these problems can still be optimally solved. One application is an optimal method for solving the two-phase Mumford-Shah functional. The third part shifts the focus to curvature regularization---where the commonly used length and area penalization 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 in the plane are compared to square ones and a method for generating adaptive meshes is introduced and evaluated. The framework is then extended to curvature regularization of surfaces. Finally, the thesis is concluded by three applications to early vision problems: cardiac MRI segmentation, image registration, and cell classification

Parallel and Distributed Graph Cuts

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 speedup, an extensive empirical evaluation on several applications with many different data sets consistently shows good performance

Generalized roof duality

AbstractThe roof dual bound for quadratic unconstrained binary optimization is the basis for several methods for efficiently computing the solution to many hard combinatorial problems. It works by constructing the tightest possible lower-bounding submodular function, and instead of minimizing the original objective function, the relaxation is minimized. However, for higher-order problems the technique has been less successful. A standard technique is to first reduce the problem into a quadratic one by introducing auxiliary variables and then apply the quadratic roof dual bound, but this may lead to loose bounds.We generalize the roof duality technique to higher-order optimization problems. Similarly to the quadratic case, optimal relaxations are defined to be the ones that give the maximum lower bound. We show how submodular relaxations can efficiently be constructed in order to compute the generalized roof dual bound for general cubic and quartic pseudo-boolean functions. Further, we prove that important properties such as persistency still hold, which allows us to determine optimal values for some of the variables. From a practical point of view, we experimentally demonstrate that the technique outperforms the state of the art for a wide range of applications, both in terms of lower bounds and in the number of assigned variables

Mesh Types for Curvature Regularization

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 two improvements to a recently proposed framework based on global optimization. The mesh geometry is analyzed both from a theoretical and experimental viewpoint and hexagonal meshes are shown to be superior. Our second contribution is that we generalize the framework to handle mean curvature regularization for 3D surface completion and segmentation

Biogödsel som kvävekälla i hydroponisk tomatproduktion

I hydroponisk produktion av tomat används nästan uteslutande lättlösliga mineralgödselmedel som näringskälla. Industriell fixering av kväve till mineralgödselmedel genom Haber-Bosch processen är en process som påverkar miljön negativt. För att den hydroponiska produktionen av tomater ska få mindre miljöpåverkan är det önskvärt att hitta organiska kvävekällor som fungerar i sådan produktion. Biogödsel från biogasverk är ett intressant alternativ. I biogödsel är det organiskt bundna kvävet mineraliserat och direkt tillgängligt för växterna i form av ammonium (NH4+). Tomatkulturen är känslig mot ammonium. Genom att låta biogödseln genomgå nitrifikation kan ammoniumkvävet omvandlas till nitratkväve (NO3-). Arbetets syfte är att undersöka hur stor del av det totala kvävebehovet i hydroponisk tomatproduktion som kan tillgodoses med biogödsel som genomgått nitrifikation. Resultatet visar att biogödsel inte kan ersätta mineralgödsel, men det kan utgöra ett komplement. Cirka en tredjedel av kvävebehovet kan tillsättas med biogödsel under kulturtiden. Hur stor del av kvävebehovet som kan tillsättas med biogödsel beror på växtnäringskoncentrationen i biogödseln, hur mycket av ammoniumkvävet i biogödseln som oxiderat till nitrat och hur receptet för näringslösningen ser ut.In hydroponic production of tomatoes easily soluble mineral fertilizers are used as a source of nitrogen. The Haber-Bosch process is used for industrial fixation of nitrogen to mineral fertilizers; the process has negative effects on the environment. In order to reduce the environmental impact of hydroponic tomato production it is desirable to find organic nitrogen sources for such production. Digestate from biogas plants is an interesting alternative. Nitrogen from organic compounds is already mineralized into ammonium (NH4+), an inorganic compound which can be utilized by the tomato plants. The tomato plant culture is sensitive to ammonium. The ammonium in the digestate can be oxidized into nitrate (NO3-) through a process known as nitrification. This essay has the purpose to investigate how much of the total nitrogen demand of a hydroponic tomato culture that can be applied with digestate which has gone through nitrification. The results show that digestate cannot fully replace mineral fertilizers, but can replace some of it. A third of the total nitrogen demand can be applied with digestate. How much of the nitrogen demand that can be applied depends on the nutrient concentration of the digestate, how much ammonium that has been oxidized into nitrate and the nutrient solution recipe

History Allies: Helping Protect Your Past: Resources on Managing Archives & Records for Community-Based Organizations

Since 2015, the UK Libraries Special Collections Research Center (UK SCRC) has offered “archives basics” workshops for community-based organizations in central Kentucky. These workshops, titled “History Allies: Helping Protect Your Past,” are free and open to the public and often hosted in partnership with area public libraries. Attendees have been from African American churches, LGBTQIA organizations, genealogical groups, museums, and more. Topics include the historical value of organizational records, selecting records for permanent retention, inventorying and storing physical and digital records, providing access to researchers, managing volunteers and volunteer projects, digitization methods and standards, and outreach and exhibits. The workshops also include opportunities for networking and small group discussion. Presenters for the workshops have included archivists and librarians from UK SCRC, Scott County (Kentucky) Public Library, and the Lexington (Kentucky) Public Library, including Sandra Baird, Ruth Bryan, Nancy DeMarcus, Sarah Dorpinghaus, Sarah Hubbard, Reinette Jones, Matthew Strandmark, Kathy Vaughan-Lloyd, and Stacie Williams

Teaching Undergraduates with Primary Sources 2020 Research Study Report

This report presents the findings of an exploratory examination of the pedagogical practices of social sciences and humanities instructors who teach undergraduates with primary sources at the University of Kentucky (UK). Conducted in December 2019 and January 2020 by a research team from the University of Kentucky Libraries Special Collections Research Center, the study reveals areas of success within existing programs and services, the benefits and drawbacks of teaching with digitized primary sources, as well as inherent pedagogical challenges to overcome. A list of recommendations based on the findings seeks to address these challenges and concludes the report. As part of the “Teaching Undergraduates with Primary Sources” research project, coordinated by ITHAKA S+R, twenty five public and private research universities and liberal arts colleges in the United States and the United Kingdom each completed similar local reports

Om rullstensbildningarne och sättet, hrarpå de blifvit danade

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