11 research outputs found

    MILP Formulations for Unsupervised and Interactive Image Segmentation and Denoising

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    Image segmentation and denoising are two key components of modern computer vision systems. The Potts model plays an important role for denoising of piecewise defined functions, and Markov Random Field (MRF) using Potts terms are popular in image segmentation. We propose Mixed Integer Linear Programming (MILP) formulations for both models, and utilize standard MILP solvers to efficiently solve them. Firstly, we investigate the discrete first derivative (piecewise constant) Potts model with the ` 1 norm data term. We propose a novel MILP formulation by introducing binary edge variables to model the Potts prior. We look into the facet-defining inequalities for the associated integer polytope. We apply the model for generating superpixels on noisy images. Secondly, we propose a MILP formulation for the discrete piecewise affine Potts model. To obtain consistent partitions, the inclusion of multicut constraints is necessary, which is added iteratively using the cutting plane method. We apply the model for simultaneously segmenting and denoising depth images. Thirdly, MILP formulations of MRF models with global connectivity constraints were investigated previously, but only simplified versions of the problem were solved. We investigate this problem via a branch-and-cut method and propose a user-interactive way for segmentation. Our proposed MILPs are in general NP-hard, but they can be used to generate globally optimal solutions and ground-truth results. We also propose three fast heuristic algorithms that provide good solutions in very short time. The MILPs can be applied as a post-processing method on top of any algorithms, not only providing a guarantee on the quality, but also seek for better solutions within the branch-and-cut framework of the solver. We demonstrate the power and usefulness of our methods by extensive experiments against other state-of-the-art methods on synthetic images, standard image datasets, as well as medical images with trained probability maps

    A First Derivative Potts Model for Segmentation and Denoising Using ILP

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    Unsupervised image segmentation and denoising are two fundamental tasks in image processing. Usually, graph based models such as multicut are used for segmentation and variational models are employed for denoising. Our approach addresses both problems at the same time. We propose a novel ILP formulation of the first derivative Potts model with the â„“1\ell_1 data term, where binary variables are introduced to deal with the â„“0\ell_0 norm of the regularization term. The ILP is then solved by a standard off-the-shelf MIP solver. Numerical experiments are compared with the multicut problem.Comment: 6 pages, 2 figures. To appear at Proceedings of International Conference on Operations Research 2017, Berli

    Graph Theoretic Algorithms Adaptable to Quantum Computing

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    Computational methods are rapidly emerging as an essential tool for understanding and solving complex engineering problems, which complement the traditional tools of experimentation and theory. When considered in a discrete computational setting, many engineering problems can be reduced to a graph coloring problem. Examples range from systems design, airline scheduling, image segmentation to pattern recognition, where energy cost functions with discrete variables are extremized. However, using discrete variables over continuous variables introduces some complications when defining differential quantities, such as gradients and Hessians involved in scientific computations within solid and fluid mechanics. Consequently, graph techniques are under-utilized in this important domain. However, we have recently witnessed great developments in quantum computing where physical devices can solve discrete optimization problems faster than most well-known classical algorithms. This warrants further investigation into the re-formulation of scientific computation problems into graph-theoretic problems, thus enabling rapid engineering simulations in a soon-to-be quantum computing world. The computational techniques developed in this thesis allow the representation of surface scalars, such as perimeter and area, using discrete variables in a graph. Results from integral geometry, specifically Cauchy-Crofton relations, are used to estimate these scalars via submodular functions. With this framework, several quantities important to engineering applications can be represented in graph-based algorithms. These include the surface energy of cracks for fracture prediction, grain boundary energy to model microstructure evolution, and surface area estimates (of grains and fibers) for generating conformal meshes. Combinatorial optimization problems for these applications are presented first. The last two chapters describe two new graph coloring algorithms implemented on a physical quantum computing device: the D-wave quantum annealer. The first algorithm describes a functional minimization approach to solve differential equations. The second algorithm describes a realization of the Boltzmann machine learning algorithm on a quantum annealer. The latter allows generative and discriminative learning of data, which has vast applications in many fields. Theoretical aspects and the implementation of these problems are outlined with a focus on engineering applications.PHDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168116/1/sidsriva_1.pd

    Fuelling the zero-emissions road freight of the future: routing of mobile fuellers

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    The future of zero-emissions road freight is closely tied to the sufficient availability of new and clean fuel options such as electricity and Hydrogen. In goods distribution using Electric Commercial Vehicles (ECVs) and Hydrogen Fuel Cell Vehicles (HFCVs) a major challenge in the transition period would pertain to their limited autonomy and scarce and unevenly distributed refuelling stations. One viable solution to facilitate and speed up the adoption of ECVs/HFCVs by logistics, however, is to get the fuel to the point where it is needed (instead of diverting the route of delivery vehicles to refuelling stations) using "Mobile Fuellers (MFs)". These are mobile battery swapping/recharging vans or mobile Hydrogen fuellers that can travel to a running ECV/HFCV to provide the fuel they require to complete their delivery routes at a rendezvous time and space. In this presentation, new vehicle routing models will be presented for a third party company that provides MF services. In the proposed problem variant, the MF provider company receives routing plans of multiple customer companies and has to design routes for a fleet of capacitated MFs that have to synchronise their routes with the running vehicles to deliver the required amount of fuel on-the-fly. This presentation will discuss and compare several mathematical models based on different business models and collaborative logistics scenarios

    International Conference on Continuous Optimization (ICCOPT) 2019 Conference Book

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    The Sixth International Conference on Continuous Optimization took place on the campus of the Technical University of Berlin, August 3-8, 2019. The ICCOPT is a flagship conference of the Mathematical Optimization Society (MOS), organized every three years. ICCOPT 2019 was hosted by the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) Berlin. It included a Summer School and a Conference with a series of plenary and semi-plenary talks, organized and contributed sessions, and poster sessions. This book comprises the full conference program. It contains, in particular, the scientific program in survey style as well as with all details, and information on the social program, the venue, special meetings, and more
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