Foreground segmentation is a common first step in tracking and surveillance applications.  The purpose of foreground segmentation is to provide later stages of image processing with an indication of where interesting data can be found.  This thesis is an investigation of how foreground segmentation can be performed in two contexts: as a pre-step to trajectory tracking and as a pre-step in indoor surveillance applications. Three methods are selected and detailed: a single Gaussian method, a Gaussian mixture model method, and a codebook method.  Experiments are then performed on typical input video using the methods.  It is concluded that the Gaussian mixture model produces the output which yields the best trajectories when used as input to the trajectory tracker.  An extension is proposed to the Gaussian mixture model which reduces shadow, improving the performance of foreground segmentation in the surveillance context.

Dans ce manuscrit de thèse, nous présentons un descripteur robuste pour la soustraction d’arrière-plan qui est capable de décrire la texture à partir d’une séquence d’images. Ce descripteur est moins sensible aux bruits et produit un histogramme court, tout en préservant la robustesse aux changements d’éclairage. Un autre descripteur pour la reconnaissance dynamique des textures est également proposé. Le descripteur permet d’extraire non seulement des informations de couleur, mais aussi des informations plus détaillées provenant des séquences vidéo. Enfin, nous présentons une approche de sélection de caractéristiques basée sur le principe d'apprentissage par ensemble qui est capable de sélectionner les caractéristiques appropriées pour chaque pixel afin de distinguer les objets de premier plan de l’arrière plan. En outre, notre proposition utilise un mécanisme pour mettre à jour l’importance relative de chaque caractéristique au cours du temps. De plus, une approche heuristique est utilisée pour réduire la complexité de la maintenance du modèle d’arrière-plan et aussi sa robustesse. Par contre, cette méthode nécessite un grand nombre de caractéristiques pour avoir une bonne précision. De plus, chaque classificateur de base apprend un ensemble de caractéristiques au lieu de chaque caractéristique individuellement. Pour compenser ces limitations, nous avons amélioré cette approche en proposant une nouvelle méthodologie pour sélectionner des caractéristiques basées sur le principe du « wagging ». Nous avons également adopté une approche basée sur le concept de « superpixel » au lieu de traiter chaque pixel individuellement. Cela augmente non seulement l’efficacité en termes de temps de calcul et de consommation de mémoire, mais aussi la qualité de la détection des objets mobiles. / In this thesis, we present a robust descriptor for background subtraction which is able to describe texture from an image sequence. The descriptor is less sensitive to noisy pixels and produces a short histogram, while preserving robustness to illumination changes. Moreover, a descriptor for dynamic texture recognition is also proposed. This descriptor extracts not only color information, but also a more detailed information from video sequences. Finally, we present an ensemble for feature selection approach that is able to select suitable features for each pixel to distinguish the foreground objects from the background ones. Our proposal uses a mechanism to update the relative importance of each feature over time. For this purpose, a heuristic approach is used to reduce the complexity of the background model maintenance while maintaining the robustness of the background model. However, this method only reaches the highest accuracy when the number of features is huge. In addition, each base classifier learns a feature set instead of individual features. To overcome these limitations, we extended our previous approach by proposing a new methodology for selecting features based on wagging. We also adopted a superpixel-based approach instead of a pixel-level approach. This does not only increases the efficiency in terms of time and memory consumption, but also can improves the segmentation performance of moving objects.

Dans ce manuscrit de thèse, nous introduisons les avancées récentes sur la décomposition en matrices (et tenseurs) de rang faible et parcimonieuse ainsi que les contributions pour faire face aux principaux problèmes dans ce domaine. Nous présentons d’abord un aperçu des méthodes matricielles et tensorielles les plus récentes ainsi que ses applications sur la modélisation d’arrière-plan et la segmentation du premier plan. Ensuite, nous abordons le problème de l’initialisation du modèle de fond comme un processus de reconstruction à partir de données manquantes ou corrompues. Une nouvelle méthodologie est présentée montrant un potentiel intéressant pour l’initialisation de la modélisation du fond dans le cadre de VSI. Par la suite, nous proposons une version « double contrainte » de l’ACP robuste pour améliorer la détection de premier plan en milieu marin dans des applications de vidéo-surveillance automatisées. Nous avons aussi développé deux algorithmes incrémentaux basés sur tenseurs afin d’effectuer une séparation entre le fond et le premier plan à partir de données multidimensionnelles. Ces deux travaux abordent le problème de la décomposition de rang faible et parcimonieuse sur des tenseurs. A la fin, nous présentons un travail particulier réalisé en conjonction avec le Centre de Vision Informatique (CVC) de l’Université Autonome de Barcelone (UAB). / This thesis introduces the recent advances on decomposition into low-rank plus sparse matrices and tensors, as well as the main contributions to face the principal issues in moving object detection. First, we present an overview of the state-of-the-art methods for low-rank and sparse decomposition, as well as their application to background modeling and foreground segmentation tasks. Next, we address the problem of background model initialization as a reconstruction process from missing/corrupted data. A novel methodology is presented showing an attractive potential for background modeling initialization in video surveillance. Subsequently, we propose a double-constrained version of robust principal component analysis to improve the foreground detection in maritime environments for automated video-surveillance applications. The algorithm makes use of double constraints extracted from spatial saliency maps to enhance object foreground detection in dynamic scenes. We also developed two incremental tensor-based algorithms in order to perform background/foreground separation from multidimensional streaming data. These works address the problem of low-rank and sparse decomposition on tensors. Finally, we present a particular work realized in conjunction with the Computer Vision Center (CVC) at Autonomous University of Barcelona (UAB).

Recent developments in the computer vision field achieve state-of-the-art performance by utilizing large-scale training datasets and in the absence of that, generating synthetic datasets of said magnitude. Yet, for certain applications, it is not feasible to synthesize high fidelity training data (e.g., biomedical computer vision domain), or to achieve detailed explainability for the program's decisions. Formulating a part-based approach can help alleviate the aforementioned challenges as (i) a scene can naturally be decomposed into a hierarchical part-based structure, and (ii) using domain knowledge by incorporating the object parts' topological and geometrical constraints reduces the complexity of learning and inference, benefiting methods in terms of data efficiency and computational resources. This dissertation investigates multiple applications that benefit from a part-based solution regarding the applications' performance metrics and/or computational efficiency. We develop part-based methods for registration, segmentation, unsupervised object discovery in large-scale image collections, and unsupervised unknown foreground discovery in streaming scenarios.

The thesis deals with an image processing problem called image matting. The problem involves detection of a foreground and background in an image with minimal user interaction using trimaps. Foreground detection is used in image composition. The goal of the thesis is to apply already known algorithms, in this case A Global sampling matting, in an Android application. The most important result is an intuitive application that can be used for making creative viral photos. Agile methodology is applied throughout the whole application development cycle. From the very beginning, the application is publicly available as a minimum viable product on Google play. The work’s contribution is in optimization of the mentioned algorithm for use in mobile devices and parallelization on a GPU, together with a publicly available application.

This thesis deals with the problem of foreground extraction on mobile devices. The main goal of this project is to find or design segmentation methods for separating a user-selected object from an image (or video). The main requirement of these methods is the image processing time and segmentation quality. Some existing solutions of this problem are mentioned and their usability on mobile devices is discussed. A mobile application is created within the project, demonstrating the implemented real time foreground extraction algorithm.

In this master thesis, object's detection in image and tracking these objects in temporal area will be presented. First, theoretical background of the image's preprocessing, image filtration, the foreground extraction, and many others various image's features will be described. Next, design and implementation of detector will be processed. This part of my master thesis containes mainly information about detection of objects on belt conveyer Finally,results, conclusion and many supplementary data such as a photography camera's location will be shown.

Text in German, summaries in German and English / Mit der vorliegenden Arbeit wird Ijob 28 in der Perspektive einer ästhetischen Theologie des Alten Testaments, wie sie vornehmlich Helmuth Utzschneider vertritt, untersucht. Entspre-chend der Grundbedeutung der Begriffe Ästhetik und Theologie stehen die Wahrnehmung im Allgemeinen und die Wahrnehmung Gottes im Besonderen im Zentrum der Betrachtung. Hierfür werden im Großen die ästhetische Gestalt und der theologische Gehalt (die Literari-zität und die Theologie, die literarische Ästhetik und die ästhetische Theologie) von Ijob 28 beleuchtet, wobei der Fokus auf dem theologischen Gehalt liegt. Die Untersuchung folgt der These, dass die Analyse von Ijob 28 in ästhetisch-theologischer Blickrichtung einen neuen Verstehenshorizont des Kapitels eröffnet. Dabei birgt das Ergebnis das Potential, Strukturen des Lebens zu heben und bewusst zu machen, um desgleichen deren Erneuerung im Horizont der Gottesfurcht herauszufordern. / This dissertation is an examination of Job 28 from the perspective of an aesthetic theology of the Old Testament, as represented primarily by Helmut Utzschneider. Following the basic meaning of the terms aesthetic and theology, the perception at large and the perception of God specifically are at the core of this exploration. Therefor the aesthetic form and the theo-logical content (the literary aspect and the theology, the literary aesthetic and the aesthetic theology) of Job 28 are examined although a greater focus is on the theological content. The thesis presented here is that an analysis of Job 28 from an aesthetic-theological perspective opens up a new level of understanding this chapter. The result has the potential to recover structures of life and make them more apparent, as well as to provoke their renewal in the light of the fear of God. / Old Testament and Ancient Near Eastern Studies / M. Th. (Old Testament)

The effective surveillance of large critical areas is crucial for their security and privacy. There is no publicly available and acceptable solution of automating this task. This thesis aims to create an application utilizing a combination of a pan-tilt robotic manipulator and a visible-spectrum camera. Based on the pan-tilt unit's position and camera's images, the application searches for semantically significant changes in the captured environment and marks these regions of interest.