Filtyp
Are we using the right potential functions in the Conditional Random Field models that are popular in the Vision community? Semantic segmentation and other pixel-level labelling tasks have made significant progress recently due to the deep learning paradigm. However, most state-of-the-art structured prediction methods also include a random field model with a hand-crafted Gaussian potential to mode
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
We develop a parametric high-resolution method for the estimation of the frequency nodes of linear combinations of complex exponentials with exponential damping. We use Kronecker's theorem to formulate the associated nonlinear least squares problem as an optimization problem in the space of vectors generating Hankel matrices of fixed rank. Approximate solutions to this problem are obtained by usin
The main idea of this paper is to use distances between camera positions to recover the trajectory of a mobile robot. We consider a mobile platform equipped with a single fixed camera using images of the floor and their associated inter-image homographies to find these distances. We show that under the assumptions that the camera is rigidly mounted with a constant tilt and travelling at a constant
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 several improvements to a recently proposed framework based on global optimization. We identify and solve an issue with extraneous arcs in the original formulation by introducing region consistency constraints. The
Low-rank matrix factorization is an essential problem in many areas including computer vision, with applications in e.g. affine structure-from-motion, photometric stereo, and non-rigid structure from motion. However, very little attention has been drawn to minimal cases for this problem or to using the minimal configuration of observations to find the solution. Minimal problems are useful when eit
This paper describes a multi-camera system designed for capture and tracking of swimmers both above and below the surface of a pool. To be able to measure the swimmer's position, the cameras need to be accurately calibrated. Images captured below the surface provide a number of challenges, mainly due to refraction and reflection effects at optical media boundaries. We present practical methods for
This work presents a study of sensor network calibration from time-difference-of-arrival (TDOA) measurements for cases when the dimensions spanned by the receivers and the transmitters differ. This could for example be if receivers are restricted to a line or plane or if the transmitting objects are moving linearly in space. Such calibration arises in several applications such as calibration of (a
The ability to quickly acquire 3D models is an essential capability needed in many disciplines including robotics, computer vision, geodesy, and architecture. In this paper we present a novel method for real-time camera tracking and 3D reconstruction of static indoor environments using an RGB-D sensor. We show that by representing the geometry with a signed distance function (SDF), the camera pose
This paper presents a complete characterization and solution to microphone position self-calibration problem for time-of-arrival (TOA) measurements. This is the problem of determining the positions of receivers and transmitters given all receiver-transmitter distances. Such calibration problems arise in application such as calibration of radio antenna networks, audio or ultra-sound arrays and WiFi
Measurements with unknown time delays are common in different applications such as microphone array, radio antenna array calibration, where the sources (e.g. sounds) are transmitted in unknown time instants. In this paper, we present a method for estimating unknown time delays from Time-Difference-of-Arrival (TDOA) measurements. We propose a novel rank constraint on a matrix depending on the measu
Recent technological advancements now allow us to obtain geographical position data for a wide range of animal movements. Here we used light-level geolocators to study the annual migration cycle in great reed warblers (Acrocephalus arundinaceus), a passerine bird breeding in Eurasia and wintering in sub-Saharan Africa. We were specifically interested in seasonal strategies in routes and schedules
In a rapidly changing world, it is of fundamental importance to understand processes constraining or facilitating adaptation through microevolution. As different traits of an organism covary, genetic correlations are expected to affect evolutionary trajectories. However, only limited empirical data are available. We investigate the extent to which multivariate constraints affect the rate of adapta
The problem of fitting models to measured data has been studied extensively, not least in the field of computer vision. A central problem in this field is the difficulty in reliably find corresponding structures and points in different images, resulting in outlier data. This thesis presents theoretical results improving the understanding of the connection between model parameter estimation and pos
In this thesis some research questions regarding durability and quality of roads has been investigated. The questions are analyzed from an image analysis point of view and aims to be a complement to existing methods for analyzing asphalt. One important factor for the durability of the asphalt layer on roads is the affinity between the stones in the asphalt and the binder that holds the stones tog
Recent work on geometric vision problems has exploited convexity properties in order to obtain globally optimal solutions. In this paper we give an overview of these developments and show the tight connections between different types of convexity and optimality conditions for a large class of multiview geometry problems. We also show how the convexity properties are closely linked to different typ
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
