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Multiple Object Tracking using K-Shortest Paths Optimization

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Mumford-Shah and Potts Regularization for Manifold-Valued Data

Martin Kurt Storath

Mumford-Shah and Potts functionals are powerful variational models for regularization which are widely used in signal and image processing; typical applications are edge-preserving denoising and segmentation. Being both non-smooth and non-convex, they are ...

Springer2016

On the various local solutions to a two-input dynamic optimization problem

Dominique Bonvin

Solving a multi-input dynamic optimization of a batch processes is a complex problem involving interactions between input variables and constraints over time. The problem gets more difficult due to the presence of local solutions that have almost the same ...

Elsevier2016

Approximation methods for large-scale spatial queueing systems

Nikolaos Geroliminis, Burak Boyaci

Different than the conventional queueing systems, in spatial queueing systems (SQS) the service rate for each customer-server pairs differs and the server that intervenes for a specific customer is not known a priori, depending on the availability of serve ...

Pergamon-Elsevier Science Ltd2015

Computational Aspects of Optimization-Based Path Following of an Unmanned Helicopter

Timm Faulwasser

This paper considers the path following of unmanned helicopters based on dynamic optimization. We assume that the helicopter is equipped with a flight control system which provides an approximation of its closed-loop dynamics. The task at hand is to derive ...

2014

Unified Detection and Tracking of Instruments during Retinal Microsurgery

Raphael Sznitman

Methods for tracking an object have generally fallen into two groups: tracking by detection and tracking through local optimization. The advantage of detection-based tracking is its ability to deal with target appearance and disappearance, but it does not ...

Ieee Computer Soc2013

KL-based Control of the Learning Schedule for Surrogate Black-Box Optimization

Ilya Loshchilov

This paper investigates the control of an ML component within the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) devoted to black-box optimization. The known CMA-ES weakness is its sample complexity, the number of evaluations of the objective fun ...

2013

Global Optimization for Multiple Agents

Boi Faltings, Brammert Ottens

In many situations, agents optimize their own operations locally but their local problems are interdependent. We consider the problem of coordinating these local problems to find a globally optimal solution. We model the coordination problem as a Distribut ...

2012

Coordination and Sampling in Distributed Constraint Optimization

Brammert Ottens

The Distributed Constraint Optimization (DCOP) framework can be used to model a wide range of optimization problems that are inherently distributed. A distributed optimization problem can be viewed as a problem distributed over a set of agents, where agent ...

EPFL2012

Synthesis of sparse arrays with focused or shaped beampattern via sequential convex optimizations

Benjamin Fuchs

An iterative procedure for the synthesis of sparse arrays radiating focused or shaped beampattern is presented. The algorithm consists in solving a sequence of weighted l(1) convex optimization problems. The method can thus be readily implemented and effic ...

Institute of Electrical and Electronics Engineers2012

Hot or Not: Interactive Content Search Using Comparisons

Amin Karbasi

In comparison-based active learning, a user searching for a target object navigates through a database in the following manner. The user is asked to select the object most similar to her target from small list of objects. A new object list is then presente ...

2012