Liver segmentation is a cumbersome task when done manually, often consuming quality time of radiologists. Use of automation in such clinical task is fundamental and the subject of most modern research. Various computer aided methods have been incorporated for this task, but it has not given optimal results due to the various challenges faced as low-contrast in the images, abnormalities in the tissues, etc. As of present, there has been significant progress in machine learning and artificial intelligence (AI) in the field of medical image processing. Though challenges exist, like image sensitivity due to different scanners used to acquire images, difference in imaging methods used, just to name a few. The following research embodies a convolutional neural network (CNN) was incorporated for this process, specifically a U-net algorithm. Predicted masks are generated on the corresponding test data and the Dice similarity coefficient (DSC) is used as a statistical validation metric for performance evaluation. Three datasets, from different scanners (two1.5 T scanners and one 3.0 T scanner), have been evaluated. The U-net performs well on the given three different datasets, even though there was limited data for training, reaching upto DSC of 0.93 for one of the datasets.

Stora arealer skog behöver övervakas. Att göra detta på ett kostnadseffektivt sätt är något som skogssektorn efterfrågar. Syftet med studien var att undersöka möjligheten att skatta skogliga variabler med satellitbilder från Sentinel 2. Korrelationen mellan granskogens uppmätta reflektans i satellitbilder från Sentinel 2 och uppmätta variablerna i fält har beräknats och analyserats. Resultatet visar att styrkan i korrelation skiljer sig mellan olika rumsliga upplösningar, vilken tid på året satellitbilderna är tagna, vilka spektrala band och vegetationsindex som används samt vilka skogliga variabler som avses uppskattas. Att använda enskilda satellitbilders värden från Sentinel 2 ger inte tillräckligt tillförlitliga data för att uppskatta skogliga variabler.

Die vorliegende Arbeit beschäftigt sich mit dem Thema „Die Wiedervereinigung Deutschlands: Das Bild der Deutschen in der tschechischen Presse“. Der 3. Oktober 2010, der 20. Jahrestag der deutschen Einheit, zeigt die große Aktualität vieler Aspekte der deutschen Frage nicht nur im deutsch-tschechischen, sondern vor allem im europäischen Kontext, der in dieser Arbeit eine herausragende Rolle spielt. Die politische Wende im Jahre 1989 und die darauf rasch folgende Wiedervereinigung Deutschlands bietet den Tschechen eine einmalige Chance, die Beziehungen zu dem deutschen Nachbarn neu zu gestalten und einen gemeinsamen Weg „nach Europa“ zu suchen. Diese emotional und politisch sehr exponierte Zeit ist theoretisch auch eine Möglichkeit, die problematische deutsch-tschechische Geschichte zu überwinden. Die im 19. und 20. Jahrhundert gewonnene historische Erfahrung der Tschechen mit Deutschland korrespondiert sehr eng mit der Hoffnung auf ein gemeinsames, friedliches Europa, in dem das vereinigte Deutschland eine seinem Potenzial angemessene - dennoch nicht dominante - Rolle einnimmt, die selbstverständlich auch auf die deutsch-tschechischen Beziehungen projiziert wird. Als Ausgangsquelle der Analyse werden Presseartikel aus drei tschechischen Zeitungen Mladá fronta Dnes, Lidové noviny und Právo von Juli 1989 bis Dezember 1990 verwendet. In diesem Untersuchungszeitraum wird versucht zu beantworten, welche Bilder der Deutschen in der tschechischen Presse vorkommen. Zu erforschen ist, ob in der Presse schon existierende, meistens auf der historischen Erfahrung des 19. und des 20. Jahrhunderts basierende Bilder konstruiert bzw. rekonstruiert werden, die für die Tschechen und Europäer als bedrohlich eingestuft werden. Oder wird ein neues Bild dargestellt, das Deutschland als Vorbild und neue europäische Großmacht präsentiert, die die europäische Vereinigung aktiv mitgestaltet und vorbehaltlos stiftet. Diese beiden polarisierenden Fragestellungen spiegeln deutlich die zwiespältige Sicht auf Deutschland, sowohl im europäischen wie auch im nachbarschaftlichen Kontext.

Tropical forests store more than half of the world’s forest carbon and are particularly threatened by deforestation and degradation processes, which together represent the second largest source of anthropogenic CO2 emissions. Consequently, tropical forests are the focus of international climate policies (i.e. Reducing emissions from deforestation and forest degradation, REDD) aiming at reducing forest-related CO2 emissions. The REDD initiative lies on our ability to map forest carbon stocks (i.e. spatial dynamics) and to detect deforestation and degradations (i.e. temporal dynamics) at large spatial scales (e.g. national, forested basin), with accuracy and precision. Remote-sensing is as a key tool for this purpose, but numerous sources of error along the carbon mapping chain makes meeting REDD criteria an outstanding challenge. In the present thesis, we assessed carbon (quantified through aboveground biomass, AGB) estimation error at the tree- and plot-level using a widely used pantropical AGB model, and at the landscape-level using a remote sensing method based on canopy texture features from very high resolution (VHR) optical data. Our objective was to better understand and reduce AGB estimation error at each level using information on large canopy tree structure, distribution and spatial organization. Although large trees disproportionally contributed to forest carbon stock, they are under-represented in destructive datasets and subject to an under-estimation bias with the pantropical AGB model. We destructively sampled 77 very large tropical trees and assembled a large (pantropical) dataset to study how variation in tree form (through crown sizes and crown mass ratio) contributed to this error pattern. We showed that the source of bias in the pantropical model was a systematic increase in the proportion of tree mass allocated to the crown in canopy trees. An alternative AGB model accounting for this phenomenon was proposed. We also propagated the AGB model bias at the plot-level and showed that the interaction between forest structure and model bias, although often overlooked, might in fact be substantial. We further analyzed the structural properties of crown branching networks in light of the assumptions and predictions of the Metabolic Theory of Ecology, which supports the power-form of the pantropical AGB model. Important deviations were observed, notably from Leonardo’s rule (i.e. the principle of area conservation), which, all else being equal, could support the higher proportion of mass in large tree crowns. A second part of the thesis dealt with the extrapolation of field-plot AGB via canopy texture features of VHR optical data. A major barrier for the development of a broad-scale forest carbon monitoring method based on canopy texture is that relationships between canopy texture and stand structure parameters (including AGB) vary among forest types and regions of the world. We investigated this discrepancy using a simulation approach: virtual canopy scenes were generated for 279 1-ha plots distributed on contrasted forest types across the tropics. We showed that complementing FOTO texture with additional descriptors of forest structure, notably on canopy openness (from a lacunarity analysis) and tree slenderness (from a bioclimatic proxy) allows developing a stable inversion frame for forest AGB at large scale. Although the approach we proposed requires further empirical validation, a first case study on a forests mosaic in the Congo basin gave promising results. Overall, this work increased our understanding of mechanisms behind AGB estimation errors at the tree-, plot- and landscape-level. It stresses the need to better account for variation patterns in tree structure (e.g. ontogenetic pattern of carbon allocation) and forest structural organization (across forest types, under different environmental conditions) to improve general AGB models, and in fine our ability to accurately map forest AGB at large scale.

Computer vision is a key component of any autonomous system. Real world computer vision applications rely on a proper and accurate detection and classification of objects. A detection algorithm that doesn’t guarantee reasonable detection accuracy is not applicable in real time scenarios where safety is the main objective. Factors that impact detection accuracy are illumination conditions and image resolution. Both contribute to degradation of objects and lead to low classifications and detection accuracy. Recent development of Convolutional Neural Networks (CNNs) based algorithms offers possibilities for low-light (LL) image enhancement and super resolution (SR) image generation which makes it possible to combine such models in order to improve image quality and increase detection accuracy. This thesis evaluates different CNNs models for SR generation and LL enhancement by comparing generated images against ground truth images. To quantify the impact of the respective model on detection accuracy, a detection procedure was evaluated on generated images. Experimental results evaluated on images selected from NoghtOwls and Caltech Pedestrian datasets proved that super resolution image generation and low-light image enhancement improve detection accuracy by a substantial margin. Additionally, it has been proven that a cascade of SR generation and LL enhancement further boosts detection accuracy. However, the main drawback of such cascades is related to an increased computational time which limits possibilities for a range of real time applications. / Datorseende är en nyckelkomponent i alla autonoma system. Applikationer för datorseende i realtid är beroende av en korrekt detektering och klassificering av objekt. En detekteringsalgoritm som inte kan garantera rimlig noggrannhet är inte tillämpningsbar i realtidsscenarier, där huvudmålet är säkerhet. Faktorer som påverkar detekteringsnoggrannheten är belysningförhållanden och bildupplösning. Dessa bidrar till degradering av objekt och leder till låg klassificerings- och detekteringsnoggrannhet. Senaste utvecklingar av Convolutional Neural Networks (CNNs) -baserade algoritmer erbjuder möjligheter för förbättring av bilder med dålig belysning och bildgenerering med superupplösning vilket gör det möjligt att kombinera sådana modeller för att förbättra bildkvaliteten och öka detekteringsnoggrannheten. I denna uppsats utvärderas olika CNN-modeller för superupplösning och förbättring av bilder med dålig belysning genom att jämföra genererade bilder med det faktiska data. För att kvantifiera inverkan av respektive modell på detektionsnoggrannhet utvärderades en detekteringsprocedur på genererade bilder. Experimentella resultat utvärderades på bilder utvalda från NoghtOwls och Caltech datauppsättningar för fotgängare och visade att bildgenerering med superupplösning och bildförbättring i svagt ljus förbättrar noggrannheten med en betydande marginal. Dessutom har det bevisats att en kaskad av superupplösning-generering och förbättring av bilder med dålig belysning ytterligare ökar noggrannheten. Den största nackdelen med sådana kaskader är relaterad till en ökad beräkningstid som begränsar möjligheterna för en rad realtidsapplikationer.

Der armenisch-türkische Konflikt ist eines der zentralen Themen in der armenischen Wochenzeitung Agos. Die Karikaturisten Aret Gicir, Ohannes Saskal und Sarkis Pacaci bebildern diesen Konflikt in der Sprache der Karikaturen. Die Zeitung ist dabei auch ein Medium für das kollektive Gedächtnis der Armenier in der Türkei und in der Diaspora. Die vorliegende Arbeit untersucht die Rolle der Karikaturen als Erinnerungsfiguren und die Rolle der Karikaturisten als Erinnerungsträger in den armenischen Medien, hier namentlich die Wochenzeitung Agos, für den kollektiven Konstruktionsprozess. Ohne Medien sind keine Erinnerungen möglich und Karikaturen sind ein Medium der Erinnerung. Die Karikaturisten Aret Gicir, Ohannes Saskal und Sarkis Pacaci verwenden unterschiedliche Techniken und Bildersprachen. Ihre Karikaturen gleichen Graffitis, Piktogrammen oder erotischen Darstellungen, sie sind Botschaften und Symbole einer kulturellen Hinterlassenschaft, so wird beispielsweise der tabuisierte Diskurs der Sexualität mit dem tabuisierten Diskurs des armenischen Genozids in der Türkei verbunden, ein Tabu wird durch ein anderes Tabu ersetzt. Die vorliegende Arbeit zeigt, dass die Karikaturisten die Funktion von modernen armenischen Aschugs, den Minnesängern der Gemeinschaft, übernehmen. Anstatt die Erinnerungen wie früher persönlich von Dorf zu Dorf tragen, oder wie im europäischen Mittelalter von Burg zu Burg, übernimmt diese Reise der Erinnerung die Zeitung Agos, um den Rezipienten mit Botschaften aus der Heimat zu versorgen. Durch die Zeitung wird der Rezipient an die (armenische) Vergangenheit erinnert. An diese Erinnerung wird versucht, die kollektive Gedächtnis der Armenier als Ersatz für das fehlende kommunikative Gedächtnis zu etablieren. / The Armenian-Turkish conflict is among the key subjects treated in the Armenian weekly Agos. The caricaturists Aret Gicir, Ohannes Saskal, and Sarkis Pacaci illustrate this conflict in the language of caricatures. The newspaper is thus also a medium for the collective memory of Armenians in Turkey and in the diaspora. This dissertation examines the role of caricatures as memory figures and the role of the caricaturists as carriers of memory in the Armenian media, here specifically the Agos weekly, for the collective process of construction. Memories are not possible without media, and caricatures are a medium of memory. The caricaturists Aret Gicir, Ohannes Saskal, and Sarkis Pacaci use different techniques and imagery. Similar to graffiti, pictograms, or erotic depictions, their caricatures are messages and symbols of a cultural legacy. The tabooed discourse on sexuality, for example, is related to the tabooed discourse of the Armenian genocide in Turkey; one taboo replaces another. This thesis shows that the caricaturists assume the function of modern Armenian ashughs, the bards of the community. Instead of the earlier practice of transmitting the memories personally from village to village, or from castle to castle, as was done in medieval Europe, this journey of remembrance is carried out by the Agos newspaper in order to provide recipients with messages from home. The newspaper serves to remind recipients of the (Armenian) past. Through this memory, an attempt is made to establish the collective memory of the Armenians as a substitute for the lacking communicative memory.

We report a class of the mathematics in which an animation technology (calculating and plotting capabilities) of the software Mathematica is utilized. This class is taught for university students in a computer laboratory during a second semester. It is our purpose to make a student realize the usefulness and the importance of mathematics easily through visualization. In addition, we hope that students will acquire a new power of mathematics needed in the 21st century. For several years, we have continued this kind of class, and have continued to investigate the effectiveness that our teaching method (especially visualization) brings in the understanding of the mathematics. In this paper, we present some of this teaching method, which is performed in our class. From the questionnaire survey, it is found that our teaching method not only convinces students that the mathematics is useful or important but also deepens the mathematic understanding of students more.

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Denna studie handlar om hur alla barn kan få möjlighet att göras delaktiga i förskolans verksamhet via olika kommunikationsformer. I studien undersöks vilka arbetssätt och tecken som förskollärare använder i tydliggörandet av barns kommunikation. Syftet är att bidra med kunskap om pedagogiska arbetssätt som kan bli tydliggörande för barns kommunikation i förskolan. Empirin har samlats in genom en kvalitativ metod, med semistrukturerade intervjuer där sex förskollärare från fem olika förskolor medverkade. Resultatet har sedan analyserats utifrån ett multimodalt och designorienterat perspektiv. I resultatet framkommer det att förskollärare använder sig utav arbetssättet tydliggörande pedagogik, med tecken som det verbala språket, TAKK, bilder och digitala verktyg i tydliggörandet av barns kommunikation.

The problem of content management of multimedia data types (e.g., image, video, graphics) is becoming increasingly important with the development of advanced multimedia applications. Traditional database management systems are inadequate for the handling of such data types. They require new techniques for query formulation, retrieval, evaluation, and navigation. In this paper we develop a knowledge-based framework for modeling and retrieving image data by content. To represent the various aspects of an image object's characteristics, we propose a model which consists of three layers: (1) Feature and Content Layer, intended to contain image visual features such as contours, shapes,etc.; (2) Object Layer, which provides the (conceptual) content dimension of images; and (3) Schema Layer, which contains the structured abstractions of images, i.e., a general schema about the classes of objects represented in the object layer. We propose two abstract languages on the basis of description logics: one for describing knowledge of the object and schema layers, and the other, more expressive, for making queries. Queries can refer to the form dimension (i.e., information of the Feature and Content Layer) or to the content dimension (i.e., information of the Object Layer). These languages employ a variable free notation, and they are well suited for the design, verification and complexity analysis of algorithms. As the amount of information contained in the previous layers may be huge and operations performed at the Feature and Content Layer are time-consuming, resorting to the use of materialized views to process and optimize queries may be extremely useful. For that, we propose a formal framework for testing containment of a query in a view expressed in our query language. The algorithm we propose is sound and complete and relatively efficient. / This is an extended version of the article in: Eleventh International Symposium on Methodologies for Intelligent Systems, Warsaw, Poland, 1999.