Euclidean reconstruction of natural underwater scenes using optic imagery sequence

The development of maritime applications require monitoring, studying and preserving of detailed and close observation on the underwater seafloor and objects. Stereo vision offers advanced technologies to build 3D models from 2D still overlapping images in a relatively inexpensive way. However, while image stereo matching is a necessary step in 3D reconstruction procedure, even the most robust dense matching techniques are not guaranteed to work for underwater images due to the challenging aquatic environment. In this thesis, in addition to a detailed introduction and research on the key components of building 3D models from optic images, a robust modified quasi-dense matching algorithm based on correspondence propagation and adaptive least square matching for underwater images is proposed and applied to some typical underwater image datasets. The experiments demonstrate the robustness and good performance of the proposed matching approach

Automated Fragmentary Bone Matching

Identification, reconstruction and matching of fragmentary bones are basic tasks required to accomplish quantification and analysis of fragmentary human remains derived from forensic contexts. Appropriate techniques for three-dimensional surface matching have received great attention in computer vision literature, and various methods have been proposed for matching fragmentary meshes; however, many of these methods lack automation, speed and/or suffer from high sensitivity to noise. In addition, reconstruction of fragementary bones along with identification in the presence of reference model to compare with in an automatic scheme have not been addressed. In order to address these issues, we used a multi-stage technique for fragment identification, matching and registration. The study introduces an automated technique for matching of fragmentary human skeletal remains for improving forensic anthropology practice and policy. The proposed technique involves creation of surfaces models for the fragmentary elements which can be done using computerized tomographic scans followed by segmentation. Upon creation of the fragmentary elements models, the models go through feature extraction technique where the surface roughness map of each model is measured using local shape analysis measures. Adaptive thesholding is then used to extract model features. A multi-stage technique is then used to identify, match and register bone fragments to their corresponding template bone model. First, extracted features are used for matching with different template bone models using iterative closest point algorithm with different positions and orientations. The best match score, in terms of minimum root-mean-square error, is used along with the position and orientation and the resulting transformation to register the fragment bone model with the corresponding template bone model using iterative closest point algorithm

Camera perspective distortion in model-based visual localisation.

114 p.This thesis starts with a proposal for a collaborative global visual localization system. Then, it centres in a specific visual localisation problem: perspective distortion in template matching.The thesis enriches 3D point cloud models with a surface normal associated with each 3D point. These normals are computed using a minimization algorithm.Based in this new model, the thesis proposes an algorithm to increase the accuracy of visual localisation. The algorithm improves for template matching processes using surface normals.The hypothesis, `Given a 3D point cloud, surface orientation of the 3D points in a template matching process increases the number of inliers points found by the localisation system, that is, perspective compensation.' is objectively proved using a ground truth model.The ground truth is achieved through the design of a framework which using computer vision and computer graphics techniques carries out experiments without the noise of a real system, and prove in an objective way the hypothesis

Self-Calibration of Multi-Camera Systems for Vehicle Surround Sensing

Multi-camera systems are being deployed in a variety of vehicles and mobile robots today. To eliminate the need for cost and labor intensive maintenance and calibration, continuous self-calibration is highly desirable. In this book we present such an approach for self-calibration of multi-Camera systems for vehicle surround sensing. In an extensive evaluation we assess our algorithm quantitatively using real-world data

Self-Calibration of Multi-Camera Systems for Vehicle Surround Sensing

Multikamerasysteme werden heute bereits in einer Vielzahl von Fahrzeugen und mobilen Robotern eingesetzt. Die Anwendungen reichen dabei von einfachen Assistenzfunktionen wie der Erzeugung einer virtuellen Rundumsicht bis hin zur Umfelderfassung, wie sie für teil- und vollautomatisches Fahren benötigt wird. Damit aus den Kamerabildern metrische Größen wie Distanzen und Winkel abgeleitet werden können und ein konsistentes Umfeldmodell aufgebaut werden kann, muss das Abbildungsverhalten der einzelnen Kameras sowie deren relative Lage zueinander bekannt sein. Insbesondere die Bestimmung der relativen Lage der Kameras zueinander, die durch die extrinsische Kalibrierung beschrieben wird, ist aufwendig, da sie nur im Gesamtverbund erfolgen kann. Darüber hinaus ist zu erwarten, dass es über die Lebensdauer des Fahrzeugs hinweg zu nicht vernachlässigbaren Veränderungen durch äußere Einflüsse kommt. Um den hohen Zeit- und Kostenaufwand einer regelmäßigen Wartung zu vermeiden, ist ein Selbstkalibrierungsverfahren erforderlich, das die extrinsischen Kalibrierparameter fortlaufend nachschätzt. Für die Selbstkalibrierung wird typischerweise das Vorhandensein überlappender Sichtbereiche ausgenutzt, um die extrinsische Kalibrierung auf der Basis von Bildkorrespondenzen zu schätzen. Falls die Sichtbereiche mehrerer Kameras jedoch nicht überlappen, lassen sich die Kalibrierparameter auch aus den relativen Bewegungen ableiten, die die einzelnen Kameras beobachten. Die Bewegung typischer Straßenfahrzeuge lässt dabei jedoch nicht die Bestimmung aller Kalibrierparameter zu. Um die vollständige Schätzung der Parameter zu ermöglichen, lassen sich weitere Bedingungsgleichungen, die sich z.B. aus der Beobachtung der Bodenebene ergeben, einbinden. In dieser Arbeit wird dazu in einer theoretischen Analyse gezeigt, welche Parameter sich aus der Kombination verschiedener Bedingungsgleichungen eindeutig bestimmen lassen. Um das Umfeld eines Fahrzeugs vollständig erfassen zu können, werden typischerweise Objektive, wie zum Beispiel Fischaugenobjektive, eingesetzt, die einen sehr großen Bildwinkel ermöglichen. In dieser Arbeit wird ein Verfahren zur Bestimmung von Bildkorrespondenzen vorgeschlagen, das die geometrischen Verzerrungen, die sich durch die Verwendung von Fischaugenobjektiven und sich stark ändernden Ansichten ergeben, berücksichtigt. Darauf aufbauend stellen wir ein robustes Verfahren zur Nachführung der Parameter der Bodenebene vor. Basierend auf der theoretischen Analyse der Beobachtbarkeit und den vorgestellten Verfahren stellen wir ein robustes, rekursives Kalibrierverfahren vor, das auf einem erweiterten Kalman-Filter aufbaut. Das vorgestellte Kalibrierverfahren zeichnet sich insbesondere durch die geringe Anzahl von internen Parametern, sowie durch die hohe Flexibilität hinsichtlich der einbezogenen Bedingungsgleichungen aus und basiert einzig auf den Bilddaten des Multikamerasystems. In einer umfangreichen experimentellen Auswertung mit realen Daten vergleichen wir die Ergebnisse der auf unterschiedlichen Bedingungsgleichungen und Bewegungsmodellen basierenden Verfahren mit den aus einer Referenzkalibrierung bestimmten Parametern. Die besten Ergebnisse wurden dabei durch die Kombination aller vorgestellten Bedingungsgleichungen erzielt. Anhand mehrerer Beispiele zeigen wir, dass die erreichte Genauigkeit ausreichend für eine Vielzahl von Anwendungen ist

Signal processing with Fourier analysis, novel algorithms and applications

Fourier analysis is the study of the way general functions may be represented or approximated by sums of simpler trigonometric functions, also analogously known as sinusoidal modeling. The original idea of Fourier had a profound impact on mathematical analysis, physics and engineering because it diagonalizes time-invariant convolution operators. In the past signal processing was a topic that stayed almost exclusively in electrical engineering, where only the experts could cancel noise, compress and reconstruct signals. Nowadays it is almost ubiquitous, as everyone now deals with modern digital signals. Medical imaging, wireless communications and power systems of the future will experience more data processing conditions and wider range of applications requirements than the systems of today. Such systems will require more powerful, efficient and flexible signal processing algorithms that are well designed to handle such needs. No matter how advanced our hardware technology becomes we will still need intelligent and efficient algorithms to address the growing demands in signal processing. In this thesis, we investigate novel techniques to solve a suite of four fundamental problems in signal processing that have a wide range of applications. The relevant equations, literature of signal processing applications, analysis and final numerical algorithms/methods to solve them using Fourier analysis are discussed for different applications in the electrical engineering/computer science. The first four chapters cover the following topics of central importance in the field of signal processing: • Fast Phasor Estimation using Adaptive Signal Processing (Chapter 2) • Frequency Estimation from Nonuniform Samples (Chapter 3) • 2D Polar and 3D Spherical Polar Nonuniform Discrete Fourier Transform (Chapter 4) • Robust 3D registration using Spherical Polar Discrete Fourier Transform and Spherical Harmonics (Chapter 5) Even though each of these four methods discussed may seem completely disparate, the underlying motivation for more efficient processing by exploiting the Fourier domain signal structure remains the same. The main contribution of this thesis is the innovation in the analysis, synthesis, discretization of certain well known problems like phasor estimation, frequency estimation, computations of a particular non-uniform Fourier transform and signal registration on the transformed domain. We conduct propositions and evaluations of certain applications relevant algorithms such as, frequency estimation algorithm using non-uniform sampling, polar and spherical polar Fourier transform. The techniques proposed are also useful in the field of computer vision and medical imaging. From a practical perspective, the proposed algorithms are shown to improve the existing solutions in the respective fields where they are applied/evaluated. The formulation and final proposition is shown to have a variety of benefits. Future work with potentials in medical imaging, directional wavelets, volume rendering, video/3D object classifications, high dimensional registration are also discussed in the final chapter. Finally, in the spirit of reproducible research we release the implementation of these algorithms to the public using Github

Affine multi-view modelling for close range object measurement

In photogrammetry, sensor modelling with 3D point estimation is a fundamental topic of research. Perspective frame cameras offer the mathematical basis for close range modelling approaches. The norm is to employ robust bundle adjustments for simultaneous parameter estimation and 3D object measurement. In 2D to 3D modelling strategies image resolution, scale, sampling and geometric distortion are prior factors. Non-conventional image geometries that implement uncalibrated cameras are established in computer vision approaches; these aim for fast solutions at the expense of precision. The projective camera is defined in homogeneous terms and linear algorithms are employed. An attractive sensor model disembodied from projective distortions is the affine. Affine modelling has been studied in the contexts of geometry recovery, feature detection and texturing in vision, however multi-view approaches for precise object measurement are not yet widely available. This project investigates affine multi-view modelling from a photogrammetric standpoint. A new affine bundle adjustment system has been developed for point-based data observed in close range image networks. The system allows calibration, orientation and 3D point estimation. It is processed as a least squares solution with high redundancy providing statistical analysis. Starting values are recovered from a combination of implicit perspective and explicit affine approaches. System development focuses on retrieval of orientation parameters, 3D point coordinates and internal calibration with definition of system datum, sensor scale and radial lens distortion. Algorithm development is supported with method description by simulation. Initialization and implementation are evaluated with the statistical indicators, algorithm convergence and correlation of parameters. Object space is assessed with evaluation of the 3D point correlation coefficients and error ellipsoids. Sensor scale is checked with comparison of camera systems utilizing quality and accuracy metrics. For independent method evaluation, testing is implemented over a perspective bundle adjustment tool with similar indicators. Test datasets are initialized from precise reference image networks. Real affine image networks are acquired with an optical system (~1M pixel CCD cameras with 0.16x telecentric lens). Analysis of tests ascertains that the affine method results in an RMS image misclosure at a sub-pixel level and precisions of a few tenths of microns in object space

Image Registration Workshop Proceedings

Automatic image registration has often been considered as a preliminary step for higher-level processing, such as object recognition or data fusion. But with the unprecedented amounts of data which are being and will continue to be generated by newly developed sensors, the very topic of automatic image registration has become and important research topic. This workshop presents a collection of very high quality work which has been grouped in four main areas: (1) theoretical aspects of image registration; (2) applications to satellite imagery; (3) applications to medical imagery; and (4) image registration for computer vision research