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Asphalt is made of a mixture of stones of different sizes and a binder called bitumen, the size distribution of the stones is determined by the recipe of the asphalt. One quality check of asphalt is to see if the real size distribution of asphalt samples is consistent with the recipe. This is usually done by first extracting the binder using methylenchloride and the sieving the stones and see how

Adverse early-life conditions have lasting effects on old-age health and mortality (Almond, 2006, Barker, 1992, Barker, 1994, Mazumder et al., 2010 and Roseboom et al., 2006). Some have even considered reductions in early life disease exposure to be a primary driver of historical mortality declines (Finch and Crimmins, 2004). Consequently, understanding the mechanisms through which early and later

Ultrasound imaging of the heart is a non-invasive method widely used for different applications. One of them is to measure the blood volume in the left ventricle at different stages of the heart cycle. This demands a proper segmentation of the left ventricle and a (semi-) automated method would decrease intra-variability as well as workload. This paper presents a semi-automated segmentation method t

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

Over the past years, massive progress has been made in the ability to collect large-scale gene expression data from a limited sample size. Combined with improvements in multiplex flow cytometry-based techniques, this has made it possible to isolate and characterize specific cellular subtypes within heterogeneous populations, with a great impact on our understanding of different biological processe

Indisputably Normalized Cuts is one of the most popular segmentation algorithms in pattern recognition and computer vision. It has been applied to a wide range of segmentation tasks with great success. A number of extensions to this approach have also been proposed, including ones that can deal with multiple classes or that can incorporate a priori information in the form of grouping constraints.

It is commonly believed that higher order smoothness should be modeled using higher order interactions. For example, 2nd order derivatives for deformable (active) contours are represented by triple cliques. Similarly, the 2nd order regularization methods in stereo predominantly use MRF models with scalar (1D) disparity labels and triple clique interactions. In this paper we advocate a largely over

In this paper we direct our attention to the problem of discretization effects in intensity transformations of images. We propose to use the Wasserstein metric (also known as the Earth mover distance) to bootstrap the transformation process. The Wasserstein metric gives a mapping between gray levels that we use to direct our image mapping. In order to spatially regularize the image mapping we appl

Calibration of network nodes using only time differences of arrival (TDOA) measurements opens up for interesting applications in wireless ad-hoc sensor networks, e.g. finding the positions of cell phones by only ambient sounds or radio. We present two novel approaches for the problem of self-calibration of network nodes using only TDOA when both receivers and transmitters are unsynchronized. We co

Multiple rotation averaging is an important problem in computer vision. The problem is challenging because of the nonlinear constraints required to represent the set of rotations. To our knowledge no one has proposed any globally optimal solution for the case of simultaneous updates of the rotations. In this paper we propose a simple procedure based on Lagrangian duality that can be used to verify

Prior work on multi-view structure from motion is dominated by sequential approaches starting from a single two-view reconstruction, then adding new images one by one. In contrast, we propose a non-sequential methodology based on rotational consistency and robust estimation using convex optimization. The resulting system is more robust with respect to (i) unreliable two-view estimations caused by

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

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

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

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 decomposit

Robust fitting of geometric models is a core problem in computer vision. The most common approach is to use a hypothesize-and-test framework, such as RANSAC. In these frameworks the model is estimated from as few measurements as possible, which minimizes the risk of selecting corrupted measurements. These estimation problems are called minimal problems, and they can often be formulated as systems

A fully automatic system for abdominal organ segmentation is presented. As a first step, an organ localization is obtained via a robust and efficient feature registration method where the center of the organ is estimated together with a region of interest surrounding the center. Then, a convolutional neural network performing voxelwise classification is applied. Two convolutional neural networks o

Recurrent use of the Housing Enabler instrument has highlighted methodological challenges of broader scientific interest, namely interactions between personal functional capacity (P) and exposures to features (here potential barriers) in the built housing environment (E). This study aimed to propose and illustrate an analytic approach, separating P × E interaction effects (here accessibility probl