A Systematic Semi-Supervised Self-adaptable Fault Diagnostics approach in an evolving environment

Fault diagnostic methods are challenged by their applications to industrial components operating in evolving environments of their working conditions. To overcome this problem, we propose a Systematic Semi-Supervised Self-adaptable Fault Diagnostics approach (4SFD), which allows dynamically selecting the features to be used for performing the diagnosis, detecting the necessity of updating the diagnostic model and automatically updating it. Within the proposed approach, the main novelty is the semi-supervised feature selection method developed to dynamically select the set of features in response to the evolving environment. An artificial Gaussian and a real world bearing dataset are considered for the verification of the proposed approach

Sampling Conditions for Conforming Voronoi Meshing by the VoroCrust Algorithm

We study the problem of decomposing a volume bounded by a smooth surface into a collection of Voronoi cells. Unlike the dual problem of conforming Delaunay meshing, a principled solution to this problem for generic smooth surfaces remained elusive. VoroCrust leverages ideas from alpha-shapes and the power crust algorithm to produce unweighted Voronoi cells conforming to the surface, yielding the first provably-correct algorithm for this problem. Given an epsilon-sample on the bounding surface, with a weak sigma-sparsity condition, we work with the balls of radius delta times the local feature size centered at each sample. The corners of this union of balls are the Voronoi sites, on both sides of the surface. The facets common to cells on opposite sides reconstruct the surface. For appropriate values of epsilon, sigma and delta, we prove that the surface reconstruction is isotopic to the bounding surface. With the surface protected, the enclosed volume can be further decomposed into an isotopic volume mesh of fat Voronoi cells by generating a bounded number of sites in its interior. Compared to state-of-the-art methods based on clipping, VoroCrust cells are full Voronoi cells, with convexity and fatness guarantees. Compared to the power crust algorithm, VoroCrust cells are not filtered, are unweighted, and offer greater flexibility in meshing the enclosed volume by either structured grids or random samples

HIERARCHICAL REGULARIZATION OF POLYGONS FOR PHOTOGRAMMETRIC POINT CLOUDS OF OBLIQUE IMAGES

ISPRS Hannover Workshop 2017 on High-Resolution Earth Imaging for Geospatial Information, HRIGI 2017, City Models, Roads and Traffic , CMRT 2017, Image Sequence Analysis, ISA 2017, European Calibration and Orientation Workshop, EuroCOW 2017, 6 - 9 June 2017, Hannover, Germany2016-2017 > Academic research: refereed > Publication in refereed journal202207 bcrcVersion of RecordPublishedC

IST Austria Thesis

Many methods for the reconstruction of shapes from sets of points produce ordered simplicial complexes, which are collections of vertices, edges, triangles, and their higher-dimensional analogues, called simplices, in which every simplex gets assigned a real value measuring its size. This thesis studies ordered simplicial complexes, with a focus on their topology, which reflects the connectedness of the represented shapes and the presence of holes. We are interested both in understanding better the structure of these complexes, as well as in developing algorithms for applications. For the Delaunay triangulation, the most popular measure for a simplex is the radius of the smallest empty circumsphere. Based on it, we revisit Alpha and Wrap complexes and experimentally determine their probabilistic properties for random data. Also, we prove the existence of tri-partitions, propose algorithms to open and close holes, and extend the concepts from Euclidean to Bregman geometries

Slantlet transform-based segmentation and α -shape theory-based 3D visualization and volume calculation methods for MRI brain tumour

Magnetic Resonance Imaging (MRI) being the foremost significant component of medical diagnosis which requires careful, efficient, precise and reliable image analyses for brain tumour detection, segmentation, visualisation and volume calculation. The inherently varying nature of tumour shapes, locations and image intensities make brain tumour detection greatly intricate. Certainly, having a perfect result of brain tumour detection and segmentation is advantageous. Despite several available methods, tumour detection and segmentation are far from being resolved. Meanwhile, the progress of 3D visualisation and volume calculation of brain tumour is very limited due to absence of ground truth. Thus, this study proposes four new methods, namely abnormal MRI slice detection, brain tumour segmentation based on Slantlet Transform (SLT), 3D visualization and volume calculation of brain tumour based on Alpha (α) shape theory. In addition, two new datasets along with ground truth are created to validate the shape and volume of the brain tumour. The methodology involves three main phases. In the first phase, it begins with the cerebral tissue extraction, followed by abnormal block detection and its fine-tuning mechanism, and ends with abnormal slice detection based on the detected abnormal blocks. The second phase involves brain tumour segmentation that covers three processes. The abnormal slice is first decomposed using the SLT, then its significant coefficients are selected using Donoho universal threshold. The resultant image is composed using inverse SLT to obtain the tumour region. Finally, in the third phase, four original ideas are proposed to visualise and calculate the volume of the tumour. The first idea involves the determination of an optimal α value using a new formula. The second idea is to merge all tumour points for all abnormal slices using the α value to form a set of tetrahedrons. The third idea is to select the most relevant tetrahedrons using the α value as the threshold. The fourth idea is to calculate the volume of the tumour based on the selected tetrahedrons. In order to evaluate the performance of the proposed methods, a series of experiments are conducted using three standard datasets which comprise of 4567 MRI slices of 35 patients. The methods are evaluated using standard practices and benchmarked against the best and up-to-date techniques. Based on the experiments, the proposed methods have produced very encouraging results with an accuracy rate of 96% for the abnormality slice detection along with sensitivity and specificity of 99% for brain tumour segmentation. A perfect result for the 3D visualisation and volume calculation of brain tumour is also attained. The admirable features of the results suggest that the proposed methods may constitute a basis for reliable MRI brain tumour diagnosis and treatments

Amélioration de la représentation géométrique 2D et 3D des agrégations de poissons en support à l'étude de leur évolution spatio-temporelle

Les systèmes d’information géographique (SIG) constituent des outils performants pour la gestion, l’analyse et la représentation de données spatiales. Ils sont utilisés dans de nombreux domaines dont en biologie marine. Parmi les phénomènes spatiaux étudiés en milieu marin, la dynamique des poissons fait l’objet de nombreuses recherches, étant donné l’importance des pêches dans l’économie des régions côtières et la nécessité de gérer ces ressources de façon durable. Un comportement clé des poissons est leur capacité à se regrouper pour former des agrégations. Une compréhension de ces agrégations est nécessaire afin d’établir des stratégies de reconstruction efficaces des ressources halieutiques en déclin. Cependant, les méthodes existantes utilisées pour représenter les agrégations de poissons ne modélisent pas ces agrégations explicitement en tant qu’objets spatiaux. De plus, malgré les capacités intéressantes offertes par les SIG actuels, ces outils sont limités en raison de la nature tridimensionnelle, dynamique et floue des agrégations de poissons dans le milieu marin. L’objectif principal de ce mémoire est de proposer de nouvelles approches pouvant permettre d’améliorer l’identification et la représentation spatiale des agrégations de poissons à des échelles régionale et locale. En 2D, l’approche proposée repose sur les modèles d’objets spatiaux flous découlant de la théorie des ensembles flous. Elle utilise une structure de données vectorielle pour représenter les limites des étendues minimale et maximale des agrégations de poissons et une structure de données matricielle pour modéliser la transition graduelle existant entre ces limites. Par ailleurs, en 3D, l’approche développée se base sur la triangulation Delaunay 3D et dynamique ainsi que sur l’algorithme de clustering 3D des α-shapes. Elle permet de détecter les agrégations contenues dans un jeu de données et de les reconstruire sous la forme d’objets 3D afin de pouvoir en étudier, par exemple, les propriétés morphologiques. L’application des approches proposées à des données halieutiques révèle plusieurs avantages et limitations qui sont discutés tout au long du mémoire.Geographic information systems (GIS) are powerful tools to manage, analyse and represent spatial data. They are used in various disciplines, including marine biology. One of the most important phenomena intensively studied by marine biologists is the dynamics of fish. This is partly because there is an increasing need for sustainable management of fisheries which are very important in the economy of coastal zones. Fish aggregations are a fundamental component of these dynamics and should be better understood to establish efficient recovery strategies in the context of declining aquatic resources. However, the traditional representations of fish aggregations do not model those aggregations explicitly as spatial objects. Moreover, despite many interesting capabilities of current GIS, these tools are unable to handle the tridimensional, dynamic and fuzzy nature of fish aggregations. The main objective of this thesis is to propose new approaches to improve the representation of fish aggregations at the regional and local scales. In 2D, the proposed approach is based on the fuzzy spatial objects models, which are based on the fuzzy sets theory. It uses a vector data structure to delineate the maximal and minimal extents of fish aggregations and a raster data structure to model the gradual transition which exists between these boundaries. In 3D, the proposed approach for the representation of fish aggregations is based on the dynamic Delaunay tetrahedralisation and the 3D α-shapes clustering algorithm. The integrated algorithm allows automatic detection of the fish aggregations contained in a dataset. 3D models also allow amongst other things the study of the morphological properties of the different aggregations. Testing these approaches with fisheries data (e.g. datasets from scientific surveys, acoustic datasets) revealed several benefits and limitations which are discussed throughout this thesis

Reconstruction of surfaces from unorganized three-dimensional point clouds

In dieser Arbeit wird ein neuer Algorithmus zur Rekonstruktion von Flächen aus dreidimensionalen Punktwolken präsentiert. Seine besonderen Eigenschaften sind die Rekonstruktion von offenen Flächen mit Rändern, Datensätzen mit variabler Punktdichte und die Behandlung von scharfen Kanten, d.h. Stellen mit unendlicher Krümmung. Es werden formale Argumente angegeben, die erklären, warum der Algorithmus korrekt arbeitet. Sie bestehen aus einer Definition von 'Rekonstruktion' und dem Beweis der Existenz von Punktmengen für die der Algorithmus erfolgreich ist. Diese mathematische Analyse konzentriert sich dabei auf kompakte Flächen mit beschränkter Krümmung und ohne Ränder. Weitere Beiträge sind die Anwendung des Flächenrekonstruktionsverfahrens für die interaktive Modellierung von Flächen und eine Prozedur für die Glättung von verrauschten Punktwolken. Zusätzlich kann der Algorithmus leicht für die lokal beschränkte Rekonstruktion eingesetzt werden, wenn nur ein Teil des Datensatzes zur Rekonstruktion herangezogen werden soll.In this thesis a new algorithm for the reconstruction of surfaces from three-dimensional point clouds is presented. Its particular features are the reconstruction of open surfaces with boundaries, data sets with variable density, and the treatment of sharp edges, that is, locations of infinite curvature. We give formal arguments which explain why the algorithm works well. They consist of a definition of 'reconstruction', and the demonstration of existence of sampling sets for which the algorithm is successful. This mathematical analysis focuses on compact surfaces of limited curvature without boundary. Further contributions are the application of the surface reconstruction algorithm for interactive shape design and a smoothing procedure for noise elimination in point clouds. Additionally, the algorithm can be easily applied for locally-restricted reconstruction if only a subset of the data set has to be considered for reconstruction