Location-aware devices are one of the examples of variety of systems that can provide trajectory data. The formal definition of a trajectory is the path of a moving object in space as a function of time. Surveillance systems can now automatically detect moving objects and provide a useful dataset for further analysis. Clustering moving objects in a given scene can provide vital information about the trajectory patterns and outliers. The trajectory of an object may contain extended data at each position where the object was detected such as size, colour, etc. The focus of this work is to find an efficient trajectory clustering solution given the most fundamental trajectory data, namely position and time. The main challenge of clustering trajectory data is to handle the length of a single trajectory. The length of a trajectory can be extremely long in some cases. Hence it may cause problems to keep trajectories in main memory or it may be very inefficient to process them. Preprocessing trajectories and simplifying them will help minimize the effects of such issues. We will use some algorithms taken from literature in conjunction with some of our own algorithms in order to cluster trajectories in an efficient manner. In an attempt to accomplish this, we have designed a representation of a trajectory Furthermore, we have designed and implemented algorithms to simplify and evaluate distances between these trajectories. Moreover, we proved that our distance function obeys triangulation properties which is beneficial for clustering algorithms. Our distance function is a variation of the Fréchet distance proposed in 1906 by Maurice René Fréchet. Additionally, we will illustrate how our work can be integrated with an incremental clustering algorithm to cluster trajectories.

The proliferation of mobile devices and advances in geo-positioning technologies has fueled the growth of location-based applications, systems and services. Many location-based applications have now gained high popularity and permeated the daily activities of mobile users. This has led to a huge amount of geo-location data generated on a daily basis, which draws significant interests in analyzing and mining ubiquitous location data, especially trajectories of mobile objects moving in road networks (MO trajectories). Mobile trajectories are complex spatio-temporal sequences of location points with varying sample sizes and varying lengths. Mining interesting patterns from large collection of complex MO trajectories presents interesting challenges and opportunities which can reveal valuable insights to the studies of human mobility in many perspectives. This dissertation research contributes original ideas and innovative techniques for mining complex trajectories from whole trajectories, from subtrajectories of significant characteristics, and from semantic location sequences within large-scale datasets of MO trajectories. Concretely, the first unique contribution of this dissertation is the development of NEAT, a three-phase road-network aware trajectory clustering framework to organize MO subtrajectories into spatial clusters representing highly dense and highly continuous traffic flows in a road network. Compared with existing trajectory clustering approaches, NEAT yields highly accurate clustering results and runs orders of magnitude faster by smartly utilizing traffic locality with respect to physical constraints of the road network, traffic flows among consecutive road segments and flow-based density of mobile traffic as well as road network based distances. The second original contribution of this dissertation is the design and development of TraceMob, a methodical and high performance framework for clustering whole trajectories of mobile objects. To our best knowledge, this is the first whole trajectory clustering system for MO trajectories in road networks. The core idea of TraceMob is to develop a road-network aware transformation algorithm that can map complex trajectories of varying lengths from a road network space into a multidimensional data space while preserving the relative distances between complex trajectories in the transformed metric space. The third novel contribution is the design and implementation of a fast and effective trajectory pattern mining algorithm TrajPod. TrajPod can extract the complete set of frequent trajectory patterns from large-scale trajectory datasets by utilizing space-efficient data structures and locality-aware spatial and temporal correlations for computational efficiency. A comprehensive performance study shows that TrajPod outperforms existing sequential pattern mining algorithms by an order of magnitude.

L'accident vasculaire cérébral et la sclérose en plaques figurent parmi les maladies neurologiques les plus destructrices du système nerveux central. L'accident vasculaire cérébral est la deuxième cause de décès et la principale cause de handicap chez l'adulte dans le monde alors que la sclérose en plaques est la maladie neurologique non traumatique la plus fréquente chez l'adulte jeune. L'imagerie par résonance magnétique est un outil important pour distinguer le tissu cérébral sain du tissu pathologique à des fins de diagnostic, de suivi de la maladie, et de prise de décision pour un traitement personnalisé des patients atteints d'accident vasculaire cérébral ou de sclérose en plaques. La prédiction de l'évolution individuelle de la maladie chez les patients atteints d'accident vasculaire cérébral ou de sclérose en plaques constitue un défi pour les cliniciens avant de donner un traitement individuel approprié. Cette prédiction est possible avec des approches statistiques appropriées basées sur des informations cliniques et d'imagerie. Toutefois, l'étiologie, la physiopathologie, les symptômes et l'évolution dans l'accident vasculaire cérébral et la sclérose en plaques sont très différents. Par conséquent, dans cette thèse, les méthodes statistiques utilisées pour ces deux maladies neurologiques sont différentes. Le premier objectif était l'identification du tissu à risque d'infarctus chez les patients atteints d'accident vasculaire cérébral. Pour cet objectif, les méthodes de classification (dont les méthodes de machine learning) ont été utilisées sur des données d'imagerie mesurées à l'admission pour prédire le risque d'infarctus à un mois. Les performances des méthodes de classification ont été ensuite comparées dans un contexte d'identification de tissu à haut risque d'infarctus à partir de données humaines codées voxel par voxel. Le deuxième objectif était de regrouper les patients atteints de sclérose en plaques avec une méthode non supervisée basée sur des trajectoires individuelles cliniques et d'imagerie tracées sur cinq ans. Les groupes de trajectoires aideraient à identifier les patients menacés d'importantes progressions et donc à leur donner des médicaments plus efficaces. Le troisième et dernier objectif de la thèse était de développer un modèle prédictif pour l'évolution du handicap individuel des patients atteints de sclérose en plaques sur la base de données démographiques, cliniques et d'imagerie obtenues a l'inclusion. L'hétérogénéité des évolutions du handicap chez les patients atteints de sclérose en plaques est un important défi pour les cliniciens qui cherchent à prévoir l'évolution individuelle du handicap. Le modèle mixte linéaire à classes latentes a été utilisé donc pour prendre en compte la variabilité individuelle et la variabilité inobservée entre sous-groupes de sclérose en plaques / Stroke and multiple sclerosis are two of the most destructive neurological diseases of the central nervous system. Stroke is the second most common cause of death and the major cause of disability worldwide whereas multiple sclerosis is the most common non-traumatic disabling neurological disease of adulthood. Magnetic resonance imaging is an important tool to distinguish healthy from pathological brain tissue in diagnosis, monitoring disease evolution, and decision-making in personalized treatment of patients with stroke or multiple sclerosis.Predicting disease evolution in patients with stroke or multiple sclerosis is a challenge for clinicians that are about to decide on an appropriate individual treatment. The etiology, pathophysiology, symptoms, and evolution of stroke and multiple sclerosis are highly different. Therefore, in this thesis, the statistical methods used for the study of the two neurological diseases are different.The first aim was the identification of the tissue at risk of infarction in patients with stroke. For this purpose, the classification methods (including machine learning methods) have been used on voxel-based imaging data. The data measured at hospital admission is performed to predict the infarction risk at one month. Next, the performances of the classification methods in identifying the tissue at a high risk of infarction were compared. The second aim was to cluster patients with multiple sclerosis using an unsupervised method based on individual clinical and imaging trajectories plotted over five 5 years. Clusters of trajectories would help identifying patients who may have an important progression; thus, to treat them with more effective drugs irrespective of the clinical subtypes. The third and final aim of this thesis was to develop a predictive model for individual evolution of patients with multiple sclerosis based on demographic, clinical, and imaging data taken at study onset. The heterogeneity of disease evolution in patients with multiple sclerosis is an important challenge for the clinicians who seek to predict the disease evolution and decide on an appropriate individual treatment. For this purpose, the latent class linear mixed model was used to predict disease evolution considering individual and unobserved subgroup' variability in multiple sclerosis

Submitted by Renata Lopes (renatasil82@gmail.com) on 2017-05-30T17:44:26Z No. of bitstreams: 1 anamaradeoliveirafigueiredo.pdf: 5190215 bytes, checksum: f9ec4e5f37ac1ca446fcef9ac91c1fb5 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-06-01T11:48:59Z (GMT) No. of bitstreams: 1 anamaradeoliveirafigueiredo.pdf: 5190215 bytes, checksum: f9ec4e5f37ac1ca446fcef9ac91c1fb5 (MD5) / Made available in DSpace on 2017-06-01T11:48:59Z (GMT). No. of bitstreams: 1 anamaradeoliveirafigueiredo.pdf: 5190215 bytes, checksum: f9ec4e5f37ac1ca446fcef9ac91c1fb5 (MD5) Previous issue date: 2015-09-10 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / O reconhecimento de ações humanas é um problema desafiador em visão computacional que tem potenciais áreas de aplicações. Para descrever o principal movimento do vídeo um novo descritor de movimento é proposto neste trabalho. Este trabalho combina dois métodos para estimar o movimento entre as imagens: casamento de blocos e de gradiente de intensidade de brilho da imagem. Neste trabalho usa-se um algoritmo de casamento de blocos de tamanho variável para extrair vetores de deslocamento, os quais contém a informação de movimento. Estes vetores são computados em uma sequência de frames obtendo a trajetória do bloco, que possui a informação temporal. Os vetores obtidos através do casamento de blocos são usados para clusterizar as trajetórias esparsas de acordo com a forma. O método proposto computa essa informação para obter tensores de orientação e gerar o descritor final. Este descritor é chamado de autodescritor porque depende apenas do vídeo de entrada. O tensor usado como descritor global é avaliado através da classificação dos vídeos das bases de dados KTH, UCF11 e Hollywood2 com o classificador não linear SVM. Os resultados indicam que este método de trajetórias esparsas é competitivo comparado ao já conhecido método de trajetórias densas, usando tensores de orientação, além de requerer menos esforço computacional. / Human action recognition is a challenging problem in Computer Vision which has many potential applications. In order to describe the main movement of the video a new motion descriptor is proposed in this work. We combine two methods for estimating the motion between frames: block matching and brightness gradient of image. In this work we use a variable size block matching algorithm to extract displacement vectors as a motion information. The cross product between the block matching vector and the gra dient is used to obtain the displacement vectors. These vectors are computed in a frame sequence, obtaining the block trajectory which contains the temporal information. The block matching vectors are also used to cluster the sparse trajectories according to their shape. The proposed method computes this information to obtain orientation tensors and to generate the final descriptor. It is called self-descriptor because it depends only on the input video. The global tensor descriptor is evaluated by classification of KTH, UCF11 and Hollywood2 video datasets with a non-linear SVM classifier. Results indicate that our sparse trajectories method is competitive in comparison to the well known dense tra jectories approach, using orientation tensors, besides requiring less computational effort.