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In this paper we present a novel, fast and accurate system for detecting the presence of cars in parking lots. The system is based on fast integral channel features and machine learning. The methods are well suited for running embedded on low performance platforms. The methods are tested on a database of nearly 700,000 images of parking spaces, where 48.5% are occupied and the rest are free. The e
The problem of estimating receiver-sender node positionsfrom measured receiver-sender distances is a key issue indifferent applications such as microphone array calibration, radioantenna array calibration, mapping and positioning using ultrawidebandand mapping and positioning using round-trip-timemeasurements between mobile phones and Wi-Fi-units. Thanks torecent research in this area we have an i
Indoor positioning for smartphone usershas received a lot of attention in recent years. Whilemany solutions have been developed, most rely on aneed for pre-deployment of infrastructure or collectingground truth data to train on. In this paper we see whatcan be done using existing WiFi-infrastructure andReceived Signal Strength from these to smartphones,not using any calibration of the signal envir
We present a minimal solver for a special kind of homography arising in applications with planar camera motion (e.g. mobile robotics applications). Since the camera motion we consider only has five degrees of freedom, an explicit parametrisation allows us to reduce the required number of point correspondences to 2.5. Using fewer point correspondences is beneficial when used together with RANSAC, b
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
ConspectusThe photophysics and photochemistry of transition metal complexes (TMCs) has long been a hot field of interdisciplinary research. Rich metal-based redox processes, together with a high variety in electronic configurations and excited-state dynamics, have rendered TMCs excellent candidates for interconversion between light, chemical, and electrical energies in intramolecular, supramolecul
Localization is an important enabling technology for many applications, such as wireless sensor networks, emergency rescue services, civil defense and transportation. Suppose that a room is equipped with several microphones (or sensors), and one person is making a sound while moving around in the room. Can one find microphone and sound source positions as well as reconstruct a room geometry? The a
T cell receptor crossreactivity with different peptide ligands and biased recognition of MHC are coupled features of antigen recognition that are necessary for the T cell's diverse functional repertoire. In the crystal structure between an autoreactive, EAE T cell clone 172.10 and myelin basic protein (1-11) presented by class II MHC I-Au, recognition of the MHC is dominated by the Vβ domain of th
This thesis explores methods for estimating 3D models using depth sensors andfinding low-rank approximations of matrices. In the first part we focus on how toestimate the movement of a depth camera and creating a 3D model of the scene.Given an accurate estimation of the camera position, we can produce dense 3Dmodels using the images obtained from the camera. We present algorithms thatare both accu
Traditional matrix factorization methods approximate high dimensional data with a low dimensional subspace. This imposes constraints on the matrix elements which allow for estimation of missing entries. A lower rank provides stronger constraints and makes estimation of the missing entries less ambiguous at the cost of measurement fit. In this paper we propose a new factorization model that further
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
Despite of having no explicit shape model, multi-atlas approaches to image segmentation have proved to be a top-performer for several diverse datasets and imaging modalities. In this paper, we show how one can directly incorporate shape regularization into the multi-atlas framework. Unlike traditional multi-atlas methods, our proposed approach does not rely on label fusion on the voxel level. Inst
In this paper we consider a mobile platform with two cameras directed towards the floor mounted the same distance from the ground, assuming planar motion and constant internal parameters. Earlier work related to this specific problem geometry has been carried out for monocular systems, and the main contribution of this paper is the generalization to a binocular system and the recovery of the relat
We present a method for complex frequency estimation in several variables, extending the classical one-dimensional ESPRIT algorithm, and consider how to work with data sampled on nonstandard domains, i.e., going beyond multirectangles.
Background: A crucial step in image fusion for intraoperative guidance during endovascular procedures is the registration of preoperative computed tomography angiography (CTA) with intraoperative Cone Beam CT (CBCT). Automatic tools for image registration facilitate the 3D image guidance workflow. However their performance is not always satisfactory. The aim of this study is to assess the accuracy
In both the Geographic Information (Geo) and Building Information Modelling (BIM) domains, it is widely acknowledged that the integration of data from both domains is beneficial and a crucial step in facing the multi-disciplinary challenges of our built environment. The result of this integration - which can broadly be termed GeoBIM -could answer questions such as identifying an appropriate Heatin
The need for digital building information is increasing, both in the form of 3D city models (as geodata) and of more detailed building information models (BIM). BIM models are mainly used in the architecture, engineering and construction industry, but have recently become interesting also for municipalities. The overall aim of this paper is to study one way of dividing a building, namely the divis
