The usage of inertial sensors has traditionally been confined primarily to the aviation and marine industry due to their associated cost and bulkiness. During the last decade, however, inertial sensors have undergone a rather dramatic reduction in both size and cost with the introduction of MEMS technology. As a result of this trend, inertial sensors have become commonplace for many applications and can even be found in many consumer products, for instance smart phones, cameras and game consoles. Due to the drift inherent in inertial technology, inertial sensors are typically used in combination with aiding sensors to stabilize andimprove the estimates. The need for aiding sensors becomes even more apparent due to the reduced accuracy of MEMS inertial sensors. This thesis discusses two problems related to using inertial sensors in combination with aiding sensors. The first is the problem of ...

Outdoor maps and navigation information delivered by modern services and technologies like Google Maps and Garmin navigators have revolutionized the lifestyle of many people. Motivated by the desire for similar navigation systems for indoor usage from consumers, advertisers, emergency rescuers/responders, etc., many indoor environments such as shopping malls, museums, casinos, airports, transit stations, offices, and schools need to be mapped. Typically, the environment is first reconstructed by capturing many point clouds from various stations and defining their spatial relationships. Currently, there is a lack of an accurate, rigorous, and speedy method for relating point clouds in indoor, urban, satellite-denied environments. This thesis presents a novel and automatic way for fusing calibrated point clouds obtained using a terrestrial laser scanner and the Microsoft Kinect by integrating them with a low-cost inertial measurement unit. The developed system, titled the Scannect, is the ...

Recent improvements in the development of inertial and visual sensors allow building small, lightweight, and cheap motion capture systems, which are becoming a standard feature of smartphones and personal digital assistants. This dissertation describes developments of new motion sensing strategies using the inertial and inertial-visual sensors. The thesis contributions are presented in two parts. The first part focuses mainly on the use of inertial measurement units. First, the problem of sensor calibration is addressed and a low-cost and accurate method to calibrate the accelerometer cluster of this unit is proposed. The method is based on the maximum likelihood estimation framework, which results in a minimum variance unbiased estimator.Then using the inertial measurement unit, a probabilistic user-independent method is proposed for pedestrian activity classification and gait analysis.The work targets two groups of applications including human activity classificationand joint human activity and ...

In this dissertation, a novel scheme performing 3D reconstruction of a scene from a 2D video sequence is presented. To this aim, first, the trajectories of the salient features in the scene are determined as a sequence of displacements via Kanade-Lukas-Tomasi tracker and Kalman filter. Then, a tentative camera trajectory with respect to a metric reference reconstruction is estimated. All frame pairs are ordered with respect to their amenability to 3D reconstruction by a metric that utilizes the baseline distances and the number of tracked correspondences between the frames. The ordered frame pairs are processed via a sequential structure-from- motion algorithm to estimate the sparse structure and camera matrices. The metric and the associated reconstruction algorithm are shown to outperform their counterparts in the literature via experiments. Finally, a mesh-based, rate- distortion efficient representation is constructed through a novel procedure ...

During the last decades, information is being gathered and processed at an explosive rate. This fact gives rise to a very important issue, that is, how to effectively and precisely describe the information content of a given source signal or an ensemble of source signals, such that it can be stored, processed or transmitted by taking into consideration the limitations and capabilities of the several digital devices. One of the fundamental principles of signal processing for decades is the Nyquist-Shannon sampling theorem, which states that the minimum number of samples needed to reconstruct a signal without error is dictated by its bandwidth. However, there are many cases in our everyday life in which sampling at the Nyquist rate results in too many data and thus, demanding an increased processing power, as well as storage requirements. A mathematical theory that emerged ...

In a world where security issues have been gaining explosive importance, face and ear recognition systems have attracted increasing attention in multiple application areas, ranging from forensics and surveillance to commerce and entertainment. While the recognition performance has been steadily improving, there are still challenging recognition scenarios and conditions, notably when facing large variations in the biometric data characteristics. Additionally, the widespread use of face and ear recognition solutions raises new security concerns, making the robustness against presentation attacks a very active field of research. Lenslet light field cameras have recently come into prominence as they are able to also capture the intensity of the light rays coming from multiple directions, thus offering a richer representation of the visual scene, notably spatio-angular information. To take benefit of this richer representation, light field cameras have recently been successfully applied, not only ...

Nowadays, richer 3D visual representation formats are emerging, notably light fields and point clouds. These formats enable new applications in many usage domains, notably virtual and augmented reality, geographical information systems, immersive communications, and cultural heritage. Recently, following major improvements in 3D visual data acquisition, there is an increasing interest in point-based visual representation, which models real-world objects as a cloud of sampled points on their surfaces. Point cloud is a 3D representation model where the real visual world is represented by a set of 3D coordinates (the geometry) over the objects with some additional attributes such as color and normals. With the advances in 3D acquisition systems, it is now possible to capture a realistic point cloud to represent a visual scene with a very high resolution. These point clouds may have up to billions of points and, thus, ...

Machine identification and recognition of human faces is a rapidly growing research area in both the academic and commercial world. Most of the research to date has concentrated on the use of two dimensional information, acquired from video cameras or photographs. The use of a three dimensional system is hoped to remove many of the problems affecting the two dimensional systems such as disruption caused by changes in the face’s orientation or changes in the ambient lighting. A three dimensional system will obviously not be influenced by orientation changes and the lighting is irrelevant, as it is the shape not the shading of the face that is important. For this system to be of practical use it is important that the process of acquiring the necessary information to generate the three dimensional surface model should not require any complex or ...

Nowadays, in a time where cities contain millions of people and where travelling across the world is becoming easier and easier, the necessity of automatically identifying a person is starting to be compelling. The physical appearance and the behavioural characteristics have been discovered useful to univocally describe a person. The analytic study of the human body measures with the aim of recognising or verifying the identity of a person, is called biometrics, literally "life measure". In the last century, several biometric traits have been investigated according to the most updated technologies available at the moment, improving recognition, computational time and memory capacity. Starting from the 90’s, research on biometrics has received a huge boost thanks to the interest raised by academic institutions, government agencies and private companies. Moreover, the diffusion of new instruments, able to perform faster analyses, and to ...

An adaptive edge-enhanced correlation based robust and real-time visual tracking framework, and two machine vision systems based on the framework are proposed. The visual tracking algorithm can track any object of interest in a video acquired from a stationary or moving camera. It can handle the real-world problems, such as noise, clutter, occlusion, uneven illumination, varying appearance, orientation, scale, and velocity of the maneuvering object, and object fading and obscuration in low contrast video at various zoom levels. The proposed machine vision systems are an active camera tracking system and a vision based system for a UGV (unmanned ground vehicle) to handle a road intersection. The core of the proposed visual tracking framework is an Edge Enhanced Back-propagation neural-network Controlled Fast Normalized Correlation (EE-BCFNC), which makes the object localization stage efficient and robust to noise, object fading, obscuration, and uneven ...

Compressed sensing (CS) is a recently introduced signal acquisition framework that goes against the traditional Nyquist sampling paradigm. CS demonstrates that a sparse, or compressible, signal can be acquired using a low rate acquisition process. Since noise is always present in practical data acquisition systems, sensing and reconstruction methods are developed assuming a Gaussian (light-tailed) model for the corrupting noise. However, when the underlying signal and/or the measurements are corrupted by impulsive noise, commonly employed linear sampling operators, coupled with Gaussian-derived reconstruction algorithms, fail to recover a close approximation of the signal. This dissertation develops robust sampling and reconstruction methods for sparse signals in the presence of impulsive noise. To achieve this objective, we make use of robust statistics theory to develop appropriate methods addressing the problem of impulsive noise in CS systems. We develop a generalized Cauchy distribution (GCD) ...

Automatic ear recognition systems have seen increased interest over recent years due to multiple desirable characteristics. Ear images used in such systems can typically be extracted from profile head shots or video footage. The acquisition procedure is contactless and non-intrusive, and it also does not depend on the cooperation of the subjects. In this regard, ear recognition technology shares similarities with other image-based biometric modalities. Another appealing property of ear biometrics is its distinctiveness. Recent studies even empirically validated existing conjectures that certain features of the ear are distinct for identical twins. This fact has significant implications for security-related applications and puts ear images on a par with epigenetic biometric modalities, such as the iris. Ear images can also supplement other biometric modalities in automatic recognition systems and provide identity cues when other information is unreliable or even unavailable. In ...

In this thesis we investigate centralized and distributed variants of sparse Bayesian learning (SBL), an effective probabilistic regression method used in machine learning. Since inference in an SBL model is not tractable in closed form, approximations are needed. We focus on the variational Bayesian approximation, as opposed to others used in the literature, for three reasons: First, it is a flexible general framework for approximate Bayesian inference that estimates probability densities including point estimates as a special case. Second, it has guaranteed convergence properties. And third, it is a deterministic approximation concept that is even applicable for high dimensional problems where non-deterministic sampling methods may be prohibitive. We resolve some inconsistencies in the literature involved in other SBL approximation techniques with regard to a proper Bayesian treatment and the incorporation of a very desired property, namely scale invariance. More specifically, ...

Standard signal processing techniques are implicitly based on the assumption that the signal lies on a regular, homogeneous domain. In practice, however, many signals lie on an irregular or inhomogeneous domain. An application area where data are naturally defined on an irregular or inhomogeneous domain is human brain neuroimaging. The goal in neuroimaging is to map the structure and function of the brain using imaging techniques. In particular, functional magnetic resonance imaging (fMRI) is a technique that is conventionally used in non-invasive probing of human brain function. This doctoral dissertation deals with the development of signal processing schemes that adapt to the domain of the signal. It consists of four papers that in different ways deal with exploiting knowledge of the signal domain to enhance the processing of signals. In each paper, special focus is given to the analysis of ...

In the modern world, we are increasingly surrounded by computation devices with communication links and one or more microphones. Such devices are, for example, smartphones, tablets, laptops or hearing aids. These devices can work together as nodes in an acoustic sensor network (ASN). Such networks are a growing platform that opens the possibility for many practical applications. ASN based speech enhancement, source localization, and event detection can be applied for teleconferencing, camera control, automation, or assisted living. For this kind of applications, the awareness of auditory objects and their spatial positioning are key properties. In order to provide these two kinds of information, novel methods have been developed in this thesis. Information on the type of auditory objects is provided by a novel real-time sound classification method. Information on the position of human speakers is provided by a novel localization ...