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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 se
In this paper, we present an approach for acquiring textured 3D models of room-sized indoor spaces using a quadrocopter. Such room models are for example useful for architects and interior designers as well as for factory planners and construction man- agers. The model is internally represented by a signed distance function (SDF) and the SDF is used to directly track the camera with respect to the
In many areas, the ability to create accurate 3D models is of great interest, for example, in computer vision, robotics, architecture, and augmented reality. In this paper we show how a textured indoor environment can be reconstructed in 3D using an RGB-D camera. Real-time performance can be achieved using a GPU. We show how the camera pose can be estimated directly using the geometry that we repr
Due to advances in technology the amount of images from portable cameras has increased tremendously in recent years. Nowadays, most new mobile phones and cars have multiple cameras. Drones and other robots are often equipped with cameras as well. To be able to capture an image is not the same as to understand the content of it. Computer vision deals with the task of giving machines the ability to
A large number of medical examinations involve images in some way. Images can be used for diagnostics, follow-up studies and treatment planning. In this thesis mathematical methods have been developed and adapted in order to analyze medical images. Several applications for different imaging modalities have been studied and the usefulness of such methods is demonstrated.A complete system for detect
The field of high dynamic range imaging addresses the problem of capturing and displaying the large range of luminance levels found in the world, using devices with limited dynamic range. In this paper we present a novel tone mapping algorithm that is based on K-means clustering. Using dynamic programming we are able to not only solve the clustering problem efficiently, but also find the global op
Professor Contact details Email: christine [dot] wamsler [at] LUCSUS [dot] lu [dot] se Phone: +46 46 222 80 80Organisation LUCSUS (Lund University Centre for Sustainability Studies) Visiting address: Josephson, Biskopsgatan 5, Lund Room number: Josephson, 219 Service point: 59 WebpageChristine Wamslers profile in Lund University research portalOther affiliations Profile area member LU Profile Area
https://www.humanrights.lu.se/christine-wamsler - 2026-05-14
Professor emeritus Contact details Email: tony [dot] huzzard [at] fek [dot] lu [dot] seOrganisation Organizational Studies Service point: 10 WebpageTony Huzzards profile in Lund University research portal Publications Displaying of publications. Sorted by year, then title. Filter by type AllBookBook chapterConference - otherConference paperDissertationJournal articleJournal article (comment)Workin
https://www.lusem.lu.se/tony-huzzard - 2026-05-14
Svenska elitidrottares mentala träningsvanor undersöktes genom en webbaserad enkätundersökning som besvarades av 47 aktiva idrottare (28 män, 19 kvinnor, ålder 16-37, M=25) inom fotboll, friidrott, handboll, ishockey och tennis. Studien visade att svenska elitidrottare i genomsnitt ägnar 41 minuter i veckan åt olika mentala träningsformer, dock med stor individuell spridning. Mängden mental träninThis paper aimed to study to what extent Swedish elite athletes engage themselves in psychological skills training (PST). The study was conducted through a web-based survey with 47 respondents (28 men, 19 women, age 16-37, M=25) from five sports; handball, ice hockey, soccer, tennis and track and field. The results showed that the athletes spend on average 41 minutes a week on different types of m
This article engages critically with one of the more original and thought-provoking efforts of recent years to respond philosophically to the current predicament of the world: Thomas Lynch’s 2019 study Apocalyptic Political Theology. While agreeing with Lynch that the urgency of our times calls for more radical approaches than cautious dialogue or reformist adaptation, it nonetheless argues that a
The evolution of Software-Defined Networking (SDN) has so far been predominantly geared towards defining and refining the abstractions on the forwarding and control planes. However, despite a maturing south-bound interface and a range of proposed network operating systems, the network management application layer is yet to be specified and standardized. It has currently poorly defined access contr
The ongoing ESA funded'SMOS + Vegetation' project combines a retrieval component that aims at further improving the SMOS VOD product with an assimilation component that aims at demonstrating the added value of this product in constraining simulated land surface fluxes of carbon dioxide. This contribution focuses on the project's modelling and assimilation component. We describe the construction of
We study the one-dimensional version of the Rudin–Osher–Fatemi (ROF) denoising model and some related TV-minimization problems. A new proof of the equivalence between the ROF model and the so-called taut string algorithm is presented, and a fundamental estimate on the denoised signal in terms of the corrupted signal is derived. Based on duality and the projection theorem in Hilbert space, the proo
Inference of CO2 surface fluxes using atmospheric CO2 observations in atmospheric inversions depends critically on accurate representation of atmospheric transport. Here we characterize regional-scale CO2 transport uncertainties due to uncertainties in meteorological fields using a mesoscale atmospheric model and an ensemble of simulations with flow-dependent transport errors. During a 1-month sum
The fact that deep learning based algorithms used for digital pathology tend to overfit to the site of the training data is well-known. Since an algorithm that does not generalize is not very useful, we have in this work studied how different data augmentation techniques can reduce this problem but also how data from different sites can be normalized to each other. For both of these approaches we
The Learning with Errors problem (LWE) is one of the main candidates for post-quantum cryptography. At Asiacrypt 2017, coded-BKW with sieving, an algorithm combining the Blum-Kalai-Wasserman algorithm (BKW) with lattice sieving techniques, was proposed. In this paper, we improve that algorithm by using different reduction factors in different steps of the sieving part of the algorithm. In the Rege
In this paper we address the problem of resource allocation for alarm traffic in industrial Internet of Things networks using massive MIMO. We formulate the general problem of how to allocate pilot signals to alarm traffic such that delivery is guaranteed, while also minimising the number of pilots reserved for alarms, thus maximising the channel resources available for other traffic, such as indu
The problem of monotone smoothing splines with bounds is formulated as a constrained minimization problem of the calculus of variations. Existence and uniqueness of solutions of this problem is proved, as well as the equivalence of it to a finite dimensional but nonlinear optimization problem. A new algorithm for computing the solution which is a spline curve, using a branch and bound technique, i
Learning a computationally efficient kernel from data is an important machine learning problem. The majority of kernels in the literature do not leverage the geometry of the data, and those that do are computationally infeasible for contemporary datasets. Recent advances in approximation techniques have expanded the applicability of the kernel methodology to scale linearly with the data size. Data
