On The Potential of Image Moments for Medical Diagnosis

Medical imaging is widely used for diagnosis and postoperative or post-therapy monitoring. The ever-increasing number of images produced has encouraged the introduction of automated methods to assist doctors or pathologists. In recent years, especially after the advent of convolutional neural networks, many researchers have focused on this approach, considering it to be the only method for diagnosis since it can perform a direct classification of images. However, many diagnostic systems still rely on handcrafted features to improve interpretability and limit resource consumption. In this work, we focused our efforts on orthogonal moments, first by providing an overview and taxonomy of their macrocategories and then by analysing their classification performance on very different medical tasks represented by four public benchmark data sets. The results confirmed that convolutional neural networks achieved excellent performance on all tasks. Despite being composed of much fewer features than those extracted by the networks, orthogonal moments proved to be competitive with them, showing comparable and, in some cases, better performance. In addition, Cartesian and harmonic categories provided a very low standard deviation, proving their robustness in medical diagnostic tasks. We strongly believe that the integration of the studied orthogonal moments can lead to more robust and reliable diagnostic systems, considering the performance obtained and the low variation of the results. Finally, since they have been shown to be effective on both magnetic resonance and computed tomography images, they can be easily extended to other imaging techniques

Contributions to Open Problems on Cable Driven Robots and Persistent Manifolds for the Synthesis of Mechanisms

Although many efforts are continuously devoted to the advancement of robotics, there are still many open and unresolved problems to be faced. This thesis, therefore, sets out to tackle some of them with the aim of scratching the surface and look a little further for new ideas or solutions. The topics covered are mainly two. The first part deals with the development and improvement of control techniques for cable-driven robots. The second focuses on the study of persistent manifolds seen as constituting aspects of theoretical kinematics. In detail, -Part I deals with cable-driven platforms. In it, both techniques for selecting cable tensions and the design of a robust controller are developed. The aim is, therefore, to enhance the two building blocks of the overall control scheme in order to improve the performance of these robots during the execution of tracking tasks. -- The first chapter introduces to open problems and recalls the main concepts necessary to understand the following chapters; -- the contribution of the second chapter consists of the introduction of the Analytic Centre. It allows the generation of continuous and differentiable tension profiles while taking into account non-linear phenomena such as friction in the computation of tensions to be applied; -- the third chapter, although still at a preliminary stage, introduces sensitivity for tension calculation methods, offering perspectives of considerable interest for tension control in the current scientific context; -- the fourth chapter proposes the design of an adaptive controller. It allows external disturbances and/or uncertainties in the model to be faced such that the task can be performed with as little error as possible. The controller architecture is the innovative peculiarity conferring autonomy to cable systems. Initially applied to counteract wind in aerial systems it is now also used for cable breakage scenarios; -- the conclusions, at first, draw together the results obtained. In addition, they emphasise the lack of the techniques introduced in order to outline possible future paths and topics that need further investigation. - Part II delves into theoretical kinematics. The discovery and classification of invariant screw systems shed light on numerous aspects of robot mobility and synthesis. Nevertheless, this generated the emergence of new ideas and questions that are still unresolved. Among them, one of the more notable concerns the identification and classification of 5-dimensional persistent manifolds. -- Similarly to the first part, the first chapter provides an overview of the problems addressed and the theoretical notions necessary to understand the subsequent contributions; -- the second chapter contributes by directly tackling the above-mentioned question by exploiting the properties of dual quaternions, the Study quadric and differential geometry. A library of 5-persistent varieties, so far missing in the literature, is presented along with theorems that complete and generalise previous ones in the literature; -- an original work, concerning line motions and synthesis of mechanisms that generate them, is reported in the third chapter as a spin-off of the studies on persistent manifolds; -- the conclusions wrap up the obtained results trying to highlight gaps and deficiencies to be dealt with in the future. Here, two small sections are dedicated to ongoing works regarding the persistence definition and the screw systems' invariants and subvariants

Conformal electromagnetic wave propagation using primal mimetic finite elements

Elektromagnetische Wellenausbreitung bildet die physikalische Grundlage für unzählige Anwendungen in verschiedenen Bereichen der heutigen Welt. Um räumliche Szenarien zu modellieren, muss der kontinuierliche Raum in geeigneter Weise in ein Rechengebiet umgewandelt werden. Üblich diskretisierte Modelle – welche auf verschiedenen Größen beruhen – berücksichtigen die Beziehungen zwischen Feldvariablen mittels Relationen, welche durch partielle Differentialgleichungen repräsentiert werden. Um mathematische Beziehungen zwischen abhängigen Variablen in zweckdienlicher Art nachzubilden, schaffen hyperkomplexe Zahlensysteme ein passendes alternatives Rahmenwerk. Dieser Ansatz bezweckt das Einbinden bestimmter Systemeigenschaften und umfasst zusätzlich zur Modellierung von Feldproblemen, bei denen alle Variablen vorkommen, auch vereinfachte Modelle. Um eine wettbewerbsfähige Alternative zur üblichen numerischen Behandlung elektromagnetischer Felder in beobachtungsorientierter Weise darzubieten, wird das elektrische und magnetische Feld elektromagnetischer Wellenfelder als eine zusammengefasste Feldgröße, eingebettet im Funktionenraum, verstanden. Dieses Vorgehen ist intuitiv, da beide Felder in der Elektrodynamik gemeinsam auftreten und direkt messbar sind. Der Schwerpunkt dieser Arbeit ist in zwei Ziele untergliedert. Auf der einen Seite wird ein umformuliertes Maxwell-System in einer metrikfreien Umgebung mittels dem sogenannten „bikomplexen Ansatz“ umfassend untersucht. Auf der anderen Seite wird eine mögliche numerische Implementierung hinsichtlich der Finite-Elemente-Methode auf modernem Wege durch Nutzung der diskreten äußeren Analysis mit Fokus auf Genauigkeitsbelange bewertet. Hinsichtlich der numerischen Genauigkeitsbewertung wird demonstriert, dass der vorgelegte Ansatz grundsätzlich eine höhere Exaktheit zeigt, wenn man ihn mit Formulierungen vergleicht, welche auf der Helmholtz-Gleichung beruhen. Diese Dissertation trägt eine generalisierte hyperkomplexe alternative Darstellung von gewöhnlichen elektrodynamischen Ausdrucksweisen zum Themengebiet der Wellenausbreitung bei. Durch die Nutzung einer direkten Formulierung des elektrischen Feldes in Verbindung mit dem magnetischen Feld wird die Rechengenauigkeit von Randwertproblemen erhöht. Um diese Genauigkeitserhöhung zu erreichen, wird eine geeignete Erweiterung der de Rham-Kohomologie unterbreitet.Electromagnetic wave propagation provides the physical basis for countless applications in various subjects of today’s world. In order to model spatial scenarios, the continuous space must be converted to an appropriate computational domain. Ordinarily discretized models – which are based on distinct quantities – consider the connection between field variables by relations which are represented by partial differential equations. To reproduce mathematical relationships between dependent variables in a convenient manner, hypercomplex number systems build a suitable alternative framework. This approach aims to incorporate certain system properties and covers, in addition to the modeling of field problems where all variables are present, also simplified models. To provide a competitive alternative to the ordinary numerical handling of electromagnetic fields in an observation-based way, the electric and magnetic field of electromagnetic wave fields is understood as only one combined field variable embedded in the function space. This procedure is intuitive since both fields occur together in electrodynamics and are directly measureable. The focus of this thesis is twofold. On the one side, a reformulated Maxwell system is broadly investigated in a metric-free environment by the use of the so-called ”bicomplex approach”. On the other side, a possible numerical implementation concerning the Finite Element Method is evaluated in a modern way by the use of discrete exterior calculus with focus on accuracy matters. Regarding the numerical accuracy evaluation, it is demonstrated that the presented approach yields a higher exactness in general when comparing it to formulations which are based on the Helmholtz equation. This thesis contributes generalized hypercomplex alternative representations of ordinary electrodynamic expressions to the topic of wave propagation. By the use of a direct formulation of the electric field in conjunction with the magnetic field, the computational accuracy of boundary value problems is improved. In order to achieve this increase of accuracy, an appropriate enhancement of the de Rham cohomology is proposed

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

NASA Tech Briefs, March 1988

Topics include: New Product Ideas; NASA TU Services; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; and Life Sciences

Computational Methods for Cognitive and Cooperative Robotics

In the last decades design methods in control engineering made substantial progress in the areas of robotics and computer animation. Nowadays these methods incorporate the newest developments in machine learning and artificial intelligence. But the problems of flexible and online-adaptive combinations of motor behaviors remain challenging for human-like animations and for humanoid robotics. In this context, biologically-motivated methods for the analysis and re-synthesis of human motor programs provide new insights in and models for the anticipatory motion synthesis. This thesis presents the author’s achievements in the areas of cognitive and developmental robotics, cooperative and humanoid robotics and intelligent and machine learning methods in computer graphics. The first part of the thesis in the chapter “Goal-directed Imitation for Robots” considers imitation learning in cognitive and developmental robotics. The work presented here details the author’s progress in the development of hierarchical motion recognition and planning inspired by recent discoveries of the functions of mirror-neuron cortical circuits in primates. The overall architecture is capable of ‘learning for imitation’ and ‘learning by imitation’. The complete system includes a low-level real-time capable path planning subsystem for obstacle avoidance during arm reaching. The learning-based path planning subsystem is universal for all types of anthropomorphic robot arms, and is capable of knowledge transfer at the level of individual motor acts. Next, the problems of learning and synthesis of motor synergies, the spatial and spatio-temporal combinations of motor features in sequential multi-action behavior, and the problems of task-related action transitions are considered in the second part of the thesis “Kinematic Motion Synthesis for Computer Graphics and Robotics”. In this part, a new approach of modeling complex full-body human actions by mixtures of time-shift invariant motor primitives in presented. The online-capable full-body motion generation architecture based on dynamic movement primitives driving the time-shift invariant motor synergies was implemented as an online-reactive adaptive motion synthesis for computer graphics and robotics applications. The last chapter of the thesis entitled “Contraction Theory and Self-organized Scenarios in Computer Graphics and Robotics” is dedicated to optimal control strategies in multi-agent scenarios of large crowds of agents expressing highly nonlinear behaviors. This last part presents new mathematical tools for stability analysis and synthesis of multi-agent cooperative scenarios.In den letzten Jahrzehnten hat die Forschung in den Bereichen der Steuerung und Regelung komplexer Systeme erhebliche Fortschritte gemacht, insbesondere in den Bereichen Robotik und Computeranimation. Die Entwicklung solcher Systeme verwendet heutzutage neueste Methoden und Entwicklungen im Bereich des maschinellen Lernens und der künstlichen Intelligenz. Die flexible und echtzeitfähige Kombination von motorischen Verhaltensweisen ist eine wesentliche Herausforderung für die Generierung menschenähnlicher Animationen und in der humanoiden Robotik. In diesem Zusammenhang liefern biologisch motivierte Methoden zur Analyse und Resynthese menschlicher motorischer Programme neue Erkenntnisse und Modelle für die antizipatorische Bewegungssynthese. Diese Dissertation präsentiert die Ergebnisse der Arbeiten des Autors im Gebiet der kognitiven und Entwicklungsrobotik, kooperativer und humanoider Robotersysteme sowie intelligenter und maschineller Lernmethoden in der Computergrafik. Der erste Teil der Dissertation im Kapitel “Zielgerichtete Nachahmung für Roboter” behandelt das Imitationslernen in der kognitiven und Entwicklungsrobotik. Die vorgestellten Arbeiten beschreiben neue Methoden für die hierarchische Bewegungserkennung und -planung, die durch Erkenntnisse zur Funktion der kortikalen Spiegelneuronen-Schaltkreise bei Primaten inspiriert wurden. Die entwickelte Architektur ist in der Lage, ‘durch Imitation zu lernen’ und ‘zu lernen zu imitieren’. Das komplette entwickelte System enthält ein echtzeitfähiges Pfadplanungssubsystem zur Hindernisvermeidung während der Durchführung von Armbewegungen. Das lernbasierte Pfadplanungssubsystem ist universell und für alle Arten von anthropomorphen Roboterarmen in der Lage, Wissen auf der Ebene einzelner motorischer Handlungen zu übertragen. Im zweiten Teil der Arbeit “Kinematische Bewegungssynthese für Computergrafik und Robotik” werden die Probleme des Lernens und der Synthese motorischer Synergien, d.h. von räumlichen und räumlich-zeitlichen Kombinationen motorischer Bewegungselemente bei Bewegungssequenzen und bei aufgabenbezogenen Handlungs übergängen behandelt. Es wird ein neuer Ansatz zur Modellierung komplexer menschlicher Ganzkörperaktionen durch Mischungen von zeitverschiebungsinvarianten Motorprimitiven vorgestellt. Zudem wurde ein online-fähiger Synthesealgorithmus für Ganzköperbewegungen entwickelt, der auf dynamischen Bewegungsprimitiven basiert, die wiederum auf der Basis der gelernten verschiebungsinvarianten Primitive konstruiert werden. Dieser Algorithmus wurde für verschiedene Probleme der Bewegungssynthese für die Computergrafik- und Roboteranwendungen implementiert. Das letzte Kapitel der Dissertation mit dem Titel “Kontraktionstheorie und selbstorganisierte Szenarien in der Computergrafik und Robotik” widmet sich optimalen Kontrollstrategien in Multi-Agenten-Szenarien, wobei die Agenten durch eine hochgradig nichtlineare Kinematik gekennzeichnet sind. Dieser letzte Teil präsentiert neue mathematische Werkzeuge für die Stabilitätsanalyse und Synthese von kooperativen Multi-Agenten-Szenarien

Robust and Accurate Camera Localisation at a Large Scale

The task of camera-based localization aims to quickly and precisely pinpoint at which location (and viewing direction) the image was taken, against a pre-stored large-scale map of the environment. This technique can be used in many 3D computer vision applications, e.g., AR/VR and autonomous driving. Mapping the world is the first step to enable camera-based localization since a pre-stored map serves as a reference for a query image/sequence. In this thesis, we exploit three readily available sources: (i) satellite images; (ii) ground-view images; (iii) 3D points cloud. Based on the above three sources, we propose solutions to localize a query camera both effectively and efficiently, i.e., accurately localizing a query camera under a variety of lighting and viewing conditions within a small amount of time. The main contributions are summarized as follows. In chapter 3, we separately present a minimal 4-point and 2-point solver to estimate a relative and absolute camera pose. The core idea is exploiting the vertical direction from IMU or vanishing point to derive a closed-form solution of a quartic equation and a quadratic equation for the relative and absolute camera pose, respectively. In chapter 4, we localize a ground-view query image against a satellite map. Inspired by the insight that humans commonly use orientation information as an important cue for spatial localization, we propose a method that endows deep neural networks with the 'commonsense' of orientation. We design a Siamese network that explicitly encodes each pixel's orientation of the ground-view and satellite images. Our method boosts the learned deep features' discriminative power, outperforming all previous methods. In chapter 5, we localize a ground-view query image against a ground-view image database. We propose a representation learning method having higher location-discriminating power. The core idea is learning discriminative image embedding. Similarities among intra-place images (viewing the same landmarks) are maximized while similarities among inter-place images (viewing different landmarks) are minimized. The method is easy to implement and pluggable into any CNN. Experiments show that our method outperforms all previous methods. In chapter 6, we localize a ground-view query image against a large-scale 3D points cloud with visual descriptors. To address the ambiguities in direct 2D--3D feature matching, we introduce a global matching method that harnesses global contextual information exhibited both within the query image and among all the 3D points in the map. The core idea is to find the optimal 2D set to 3D set matching. Tests on standard benchmark datasets show the effectiveness of our method. In chapter 7, we localize a ground-view query image against a 3D points cloud with only coordinates. The problem is also known as blind Perspective-n-Point. We propose a deep CNN model that simultaneously solves for both the 6-DoF absolute camera pose and 2D--3D correspondences. The core idea is extracting point-wise 2D and 3D features from their coordinates and matching 2D and 3D features effectively in a global feature matching module. Extensive tests on both real and simulated data have shown that our method substantially outperforms existing approaches. Last, in chapter 8, we study the potential of using 3D lines. Specifically, we study the problem of aligning two partially overlapping 3D line reconstructions in Euclidean space. This technique can be used for localization with respect to a 3D line database when query 3D line reconstructions are available (e.g., from stereo triangulation). We propose a neural network, taking Pluecker representations of lines as input, and solving for line-to-line matches and estimate a 6-DoF rigid transformation. Experiments on indoor and outdoor datasets show that our method's registration (rotation and translation) precision outperforms baselines significantly

Colour local feature fusion for image matching and recognition

This thesis investigates the use of colour information for local image feature extraction. The work is motivated by the inherent limitation of the most widely used state of the art local feature techniques, caused by their disregard of colour information. Colour contains important information that improves the description of the world around us, and by disregarding it; chromatic edges may be lost and thus decrease the level of saliency and distinctiveness of the resulting grayscale image. This thesis addresses the question of whether colour can improve the distinctive and descriptive capabilities of local features, and if this leads to better performances in image feature matching and object recognition applications. To ensure that the developed local colour features are robust to general imaging conditions and capable for real-world applications, this work utilises the most prominent photometric colour invariant gradients from the literature. The research addresses several limitations of previous studies that used colour invariants, by implementing robust local colour features in the form of a Harris-Laplace interest region detection and a SIFT description which characterises the detected image region. Additionally, a comprehensive and rigorous evaluation is performed, that compares the largest number of colour invariants of any previous study. This research provides for the first time, conclusive findings on the capability of the chosen colour invariants for practical real-world computer vision tasks. The last major aspect of the research involves the proposal of a feature fusion extraction strategy, that uses grayscale intensity and colour information conjointly. Two separate fusion approaches are implemented and evaluated, one for local feature matching tasks and another approach for object recognition. Results from the fusion analysis strongly indicate, that the colour invariants contain unique and useful information that can enhance the performance of techniques that use grayscale only based features