This thesis considers the problem of tracking an object in world coordinates using measurements obtained from multiple uncalibrated cameras. A general approach to track the location of a target involves different phases including calibrating the camera, detecting the object's feature points over frames, tracking the object over frames and analyzing object's motion and behavior. The approach contains two stages. First, the problem of camera calibration using a calibration object is studied. This approach retrieves the camera parameters from the known locations of ground data in 3D and their corresponding image coordinates. The next important part of this work is to develop an automated system to estimate the trajectory of the object in 3D from image sequences. This is achieved by combining, adapting and integrating several state-of-the-art algorithms. Synthetic data based on a nearly constant velocity object motion model is used to evaluate the performance of camera calibration and state estimation algorithms.

With the drone industry's recent explosive advancement, aerial photography is becoming increasingly important for an array of applications ranging from construction to agriculture. A drone flyover can give a better overview of regions that are difficult to navigate, and is often significantly faster, cheaper and more accurate than man-made sketches and other alternatives. With this increased use comes a growing need for image processing methods to help in analyzing captured photographs. This thesis presents a method for automatic location detection in aerial photographs using databases of aerial photographs and satellite images. The proposed pipeline is based on an initial round of tests, performed by using existing feature detection, description and matching algorithms on aerial photographs with a high degree of similarity. After which further modifications and improvements were implemented to make the method functional also for handling aerial photographs with a high level of inherent differences, e.g., viewpoint changes, different camera- and lens parameters, temporary objects and weather effects. The method is shown to yield highly accurate results in geographical regions containing features with a low level of ambiguity, and where factors like viewpoint difference are not too extreme. In particular, the method has been most successful in cities and some types of farmland, producing very good results compared to methods based on camera parameters and GPS-location, which have been common in automatic location detection previously. Knowledge of these parameters is not necessary when applying the method, making it applicable more generally and also independently of the precision of the instruments used to determine said parameters.  Furthermore, the approach is extended for automatic processing of video streams. With lack of available ground truth data, no definite conclusions about absolute accuracy of the method can be drawn for this use case. But it is nevertheless clear that processing speeds can be greatly improved by making use of the fact that subsequent video snapshots have a large graphical overlap. And it can indeed also be said that, for the tested video stream, using a type of extrapolation can greatly reduce the risk of graphical noise making location detection impossible for any given snapshot. / Då drönarindustrin växer så det knakar, har flygfoton blivit allt viktigare för en rad applikationer i vårt samhälle. Att flyga över ett svårnavigerat område med en drönare kan ge bättre översikt och är ofta snabbare, billigare och mer precist än skisser eller andra alternativa översiktsmetoder. Med denna ökade användning kommer också ett ökat behov av automatisk bildprocessering för att hjälpa till i analysen av dessa fotografier. Denna avhandling presenterar en metod för automatisk positionsbedömning av flygfoton, med hjälp av databaser med flygfoton och satellitfoton. Den presenterade metoden är baserad på inledande tester av existerande feature detection, feature description och feature matching algoritmer på ett något förenklat problem, där givna foton är väldigt grafiskt lika. Efter detta implementerades ytterligare modifikationer och förbättringar för att göra metoden mer robust även för bilder med en hög nivå av grafisk diskrepans, exempelvis skillnad i synvinkel, kamera- och linsparametrar, temporära objekt och vädereffekter. Den föreslagna metoden ger nöjaktiga resultat i geografiska regioner med en proportionellt stor mängd grafiska särdrag som enkelt kan särskiljas från varandra och där den grafiska diskrepansen inte är allt för stor. Särskilt goda resultat ses i bland annat städer och vissa typer av jordbruksområden, där metoden kan ge betydligt bättre resultat än metoder baserade på kända kameraparametrar och fotografens GPS-positionering, vilket har varit ett vanligt sätt att utföra denna typ av automatisk positionsbestämning tidigare. Dessutom är den presenterade metoden ofta enklare att applicera, då precisionen för diverse mätinstrument som annars måste användas när fotot tas inte spelar in alls i metodens beräkningar. Dessutom har metoden utökats för automatisk processering av videoströmmar. På grund av bristfälligt referensdata kan inga definitiva slutsatser dras angående metodens precision för detta användningsområde. Men det är ändå tydligt att beräkningstiden kan minskas drastiskt genom att använda faktumet att två påföljande ögonblicksbilder har ett stort grafiskt överlapp. Genom att använda en sorts extrapolering kan inverkan från grafiskt brus också minskas, brus som kan göra positionsbestämning omöjligt för en given ögonblicksbild.

Nous proposons dans cette thèse une nouvelle approche cumulative de recalage d'images basée sur des primitives construites à partir des lignes de niveaux. Les lignes de niveaux sont invariantes par rapport aux diverses perturbations affectant l'image tels que les changements de contraste. Par ailleurs, leur abondance dans une image suggère naturellement un processus de décision cumulatif. Nous proposons alors un algorithme récursif d'extraction des lignes de niveaux simple et efficace qui extrait les lignes par groupes rectiligne appelés ``segments''. Les segments sont ensuite groupés -- sous contrainte de proximité -- en fonction du modèle de transformation recherchée et afin de faciliter le calcul des invariants. Les primitives construites ont alors la forme de Z, Y ou W et sont classées en fonction de leur fiabilité, ce qui participe au paramétrage du processus de décision cumulatif. Le vote est multi-tours et constitué d'une phase préliminaire de construction de listes de préférences inspiré de la technique des mariages stables. Les primitives votent à une itération donnée en fonction de leur fiabilité. Chaque itération fournit ainsi un estimé de la transformation recherchée que le tour suivant peut raffiner. Ce procédé multi-tours permet, de ce fait, d'éliminer les ambiguïtés d'appariement générées par les motifs répétitifs présents dans les images. Notre approche a été validée pour recaler des images sous différents modèles de transformations allant de la plus simple (similarité) à la plus complexe (projective). Nous montrons dans cette thèse comment le choix pertinent de primitives basées sur les lignes de niveaux en conjonction avec un processus de décision cumulatif permet d'obtenir une méthode de recalage d'images robuste, générique et complète, fournissant alors différents niveaux de précision et pouvant ainsi s'appliquer à différents contextes. / In this thesis, we propose a new image registration method that relies on level-line primitives. Level-lines are robust towards contrast changes and proposed primitives inherit their robustness. Moreover, their abundance in the image is well adapted to a cumulative matching process based on a multi-stage primitive election procedure. We propose a simple recursive tracking algorithm to extract level lines by straight sets called "segments". Segments are then grouped under proximity constraints to construct primitives (Z, Y and W shapes) that are classified into categories according to their reliability. Primitive shapes are defined according to the transformation model. The cumulative process is based on a preliminary step of preference lists construction that is inspired from the stable marriage matching algorithm. Primitives vote in a given voting stage according to their reliability. Each stage provides a coarse estimate of the transformation that the next stage gets to refine. This process, in turn, eliminate gradually the ambiguity happened by incorrect correspondences. Our additional contribution is to validate further geometric transformations, from simple to complex ones, completing the path "similarity, affine, projective". We show in this thesis how the choice of level lines in conjunction with a cumulative decision process allows defining a complete robust registration approach that is tested and evaluated on several real image sequences including different type of transformations.

Submitted by Maria Aparecida (cidazen@gmail.com) on 2017-04-27T13:46:48Z No. of bitstreams: 1 Caio Eduardo Falcão Matos.pdf: 1884390 bytes, checksum: a5489f8f52a87e6c5958458ed5470488 (MD5) / Made available in DSpace on 2017-04-27T13:46:48Z (GMT). No. of bitstreams: 1 Caio Eduardo Falcão Matos.pdf: 1884390 bytes, checksum: a5489f8f52a87e6c5958458ed5470488 (MD5) Previous issue date: 2017-02-08 / Breast cancer is one of the leading causes of death among women over the world. The high mortality rates that cancers achieves across the world highlight the importance of developing and investigating the means for the early detection and diagnosis of this disease. Computer Detection and Diagnosis Systems (Computer Assisted Detection / Diagnosis) have been used and proposed as a way to help health professionals. This work proposes a new methodology for discriminating patterns of malignancy and benignity of masses in mammography images by analysis of local characteristics. To do so, it is proposed a combined methodology of feature detectors and descriptors with a model of data representation for an analysis. The goal is to capture both texture and geometry in areas of mammograms. We use the SIFT, SURF and ORB detectors, and the descriptors HOG, LBP, BRIEF and Haar Wavelet. The generated characteristics are coded by a bag of features model to provide a new representation of the data and therefore decrease a dimensionality of the space of characteristics. Finally, this new representation is classified using three approaches: Support Vector Machine, Random Forest, and Adaptive Boosting to differentiate as malignant and benign masses. The methodology provides promising results for the diagnosis of malignant and benign mass encouraging that as local characteristics generated by descriptors and detectors produce a satisfactory a discriminating set. / O câncer de mama é apontado como uma das principais causas de morte entre as mulheres. As altas taxas de mortalidade e registros de ocorrência desse câncer em todo o mundo evidenciam a importância do desenvolvimento e investigação de meios para a detecção e diagnóstico precoce dessa doença. Sistemas de Detecção e Diagnóstico auxiliados por computador (Computer Aided Detection/Diagnosis) vêm sendo usados e propostos como forma de auxílio aos profissionais de saúde. Este trabalho propõe uma metodologia para discriminação de padrões de malignidade e benignidade de massas em imagens de mamografia através da análise de características locais. Para tanto, a metodologia combina detectores e descritores de características locais com um modelo de representação de dados para a análise, tanto de textura quanto de geometria em regiões extraídas das mamografias. São utilizados os detectores SIFT, SURF e ORB, e descritores HOG, LBP, BRIEF e Haar Wavelet. Com as características geradas é aplicado o modelo Bag of Features em uma etapa de representação que objetiva prover nova representação dos dados e por conseguinte diminuir a dimensionalidade do espaço de características. Por fim, esta nova representação é classificada utilizando três abordagens: Máquina de Vetores de Suporte, Random Forests e Adaptive Boosting visando diferenciar as massas malignas e benignas. A metodologia contém resultados promissores para o diagnóstico de massas malignas e benignas fomentando que as características locais geradas pelos descritores e detectores produzem um conjunto descriminate satisfatório.

No description available.

Neuromorphic information processing systems mimic the dynamics of neurons and synapses, and the architecture of biological nervous systems. By using a combination of sub-threshold analog circuits, and fast programmable digital circuits, spiking neural networks with co-localized memory and computation can be implemented, enabling more energy-efficient information processing than conventional von Neumann digital computers. When configuring such a spiking neural network, the variability caused by device mismatch of the analog electronic circuits must be managed and exploited. While pre-trained spiking neural networks have been approximated in neuromorphic processors in previous work, configuration methods and tools need to be developed that make efficient use of the high number of inhomogeneous analog neuron and synapse circuits in a systematic manner. The aim of the work presented here is to investigate such automatic configuration methods, focusing in particular on Monte Carlo methods, and to develop software for training and configuration of the Dynap-SE neuromorphic processor, which is based on the Dynamic Neuromorphic Asynchronous Processor (DYNAP) architecture. A Monte Carlo optimization method enabling configuration of spiking neural networks on the Dynap-SE is developed and tested with the Metropolis-Hastings algorithm in the low-temperature limit. The method is based on a hardware-in-the-loop setup where a PC performs online optimization of a Dynap-SE, and the resulting system is tested by reproducing properties of small neural networks in the auditory system of field crickets. It is shown that the system successfully configures two different auditory neural networks, consisting of three and four neurons respectively. However, appropriate bias parameter values defining the dynamic properties of the analog neuron and synapse circuits must be manually defined prior to optimization, which is time consuming and should be included in the optimization protocol in future work.

Correspondance de forme est un problème fondamental dans de nombreuses disciplines de recherche, tels que la géométrie algorithmique, vision par ordinateur et l'infographie. Communément définie comme un problème de trouver injective/ multivaluée correspondance entre une source et une cible, il constitue une tâche centrale dans de nombreuses applications y compris le transfert de attributes, récupération des formes etc. Dans récupération des formes, on peut d'abord calculer la correspondance entre la forme de requête et les formes dans une base de données, puis obtenir le meilleure correspondance en utilisant une mesure de qualité de correspondance prédéfini. Il est également particulièrement avantageuse dans les applications basées sur la modélisation statistique des formes. En encapsulant les propriétés statistiques de l'anatomie du sujet dans le model de forme, comme variations géométriques, des variations de densité, etc., il est utile non seulement pour l'analyse des structures anatomiques telles que des organes ou des os et leur variations valides, mais aussi pour apprendre les modèle de déformation de la classe d'objets. Dans cette thèse, nous nous intéressons à une enquête sur une nouvelle méthode d'appariement de forme qui exploite grande redondance de l'information à partir des ensembles de données dynamiques, variables dans le temps. Récemment, une grande quantité de recherches ont été effectuées en infographie sur l'établissement de correspondances entre les mailles statiques (Anguelov, Srinivasan et al. 2005, Aiger, Mitra et al. 2008, Castellani, Cristani et al. 2008). Ces méthodes reposent sur les caractéristiques géométriques ou les propriétés extrinsèques/intrinsèques des surfaces statiques (Lipman et Funkhouser 2009, Sun, Ovsjanikov et al. 2009, Ovsjanikov, Mérigot et al. 2010, Kim, Lipman et al., 2011) pour élaguer efficacement les paires. Bien que l'utilisation de la caractéristique géométrique est encore un standard d'or, les méthodes reposant uniquement sur l'information statique de formes peuvent générer dans les résultats de correspondance grossièrement trompeurs lorsque les formes sont radicalement différentes ou ne contiennent pas suffisamment de caractéristiques géométriques. [...] / 3D geometry modelling tools and 3D scanners become more enhanced and to a greater degree affordable today. Thus, development of the new algorithms in geometry processing, shape analysis and shape correspondence gather momentum in computer graphics. Those algorithms steadily extend and increasingly replace prevailing methods based on images and videos. Non-rigid shape correspondence or deformable shape matching has been a long-studied subject in computer graphics and related research fields. Not to forget, shape correspondence is of wide use in many applications such as statistical shape analysis, motion cloning, texture transfer, medical applications and many more. However, robust and efficient non-rigid shape correspondence still remains a challenging task due to fundamental variations between individual subjects, acquisition noise and the number of degrees of freedom involved in correspondence search. Although dynamic 2D/3D intra-subject shape correspondence problem has been addressed in the rich set of previous methods, dynamic inter-subject shape correspondence received much less attention. The primary purpose of our research is to develop a novel, efficient, robust deforming shape analysis and correspondence framework for animated meshes based on their dynamic and motion properties. We elaborate our method by exploiting a profitable set of motion data exhibited by deforming meshes with time-varying embedding. Our approach is based on an observation that a dynamic, deforming shape of a given subject contains much more information rather than a single static posture of it. That is different from the existing methods that rely on static shape information for shape correspondence and analysis.Our framework of deforming shape analysis and correspondence of animated meshes is comprised of several major contributions: a new dynamic feature detection technique based on multi-scale animated mesh’s deformation characteristics, novel dynamic feature descriptor, and an adaptation of a robust graph-based feature correspondence approach followed by the fine matching of the animated meshes. [...]

The problematics of precise pointing and more specifically an attitude control is present sincethe first days of flight and Aerospace engineering. The precise attitude control is a matter ofnecessity for a great variety of applications. In the air, planes or unmanned aerial vehicles needto be able to orient precisely. In Space, a telescope or a satellite relies on the attitude control toreach the stars or survey the Earth. The attitude control can be based on various principles, pre-calculated variables, and measurements. It is common to use the gyroscope, Sun/Star/horizonsensors for attitude determination. While those technologies are well established in the indus-try, the rise in a computational power and efficiency in recent years enabled processing of aninfinitely more rich source of information - the vision. In this Thesis, a visual system is used forthe attitude determination and is blended together with a control algorithm to form a VisionBased Attitude Control system.A demonstrator is designed, build and programmed for the purpose of Vision Based AttitudeControl. It is based on the principle of Visual servoing, a method that links image measure-ments to the attitude control, in a form of a set of joint velocities. The intermittent steps arethe image acquisition and processing, feature detection, feature tracking and the computationof joint velocities in a closed loop control scheme. The system is then evaluated in a barrage ofpartial experiments.The results show, that the used detection algorithms, Shi&Tomasi and Harris, performequally well in feature detection and are able to provide a high amount of features for tracking.The pyramidal implementation of the Lucas&Kanade tracking algorithm proves to be a capablemethod for a reliable feature tracking, invariant to rotation and scale change. To further evaluatethe Visual servoing a complete demonstrator is tested. The demonstrator shows the capabilityof Visual Servoing for the purpose of Vision Based Attitude Control. An improvement in thehardware and implementation is recommended and planned to push the system beyond thedemonstrator stage into an applicable system.

Modern unsteady (multi-)field visualizations require an effective reduction of the data to be displayed. From a huge amount of information the most informative parts have to be extracted. Instead of the fuzzy application dependent notion of feature, a new approach based on information theoretic concepts is introduced in this paper to detect important regions. This is accomplished by extending the concept of local statistical complexity from finite state cellular automata to discretized (multi-)fields. Thus, informative parts of the data can be highlighted in an application-independent, purely mathematical sense. The new measure can be applied to unsteady multifields on regular grids in any application domain. The ability to detect and visualize important parts is demonstrated using diffusion, flow, and weather simulations.

The goal of this thesis is to get familiarized with software component Microsoft .NET Framework and the problems of methods of interest point detection. On the basis of this knowledge, it is required to develop a method for the evaluation of used detectors and then implement a console application that is simply able to evaluate the results of interest point detection methods. Such evaluation is important in the process of development of the algorithms for the detection using projected method of evaluation. The interest points are searched in several images which represent the same scene from different angles of view. The requirements also inculde creation of graphic user interface that allows an easy way to setup the evaluation conditions.