Exploring Urban Spaces in the Young Imagination makes the case for taking seriously children's and young adult fiction as a topic worthy of scholarly analysis beyond a purely didactic focus. The recent explosion of interest in this literature among adult readers and the blurring of boundaries between adult and youth media demonstrate that it has become a powerful influence on popular culture and has captured the collective imagination. As the future inhabitants of urban spaces, paying close attention to children’s and youth perspectives can provide fresh lenses, with which to view the cultural construction of cityscapes. This dissertation examines post-millennium children’s and YA fiction and film. Drawing on theories of the spatial turn, my research provides five case studies on a variety of topics related to the contemporary metropolis – ranging from cognitive disability to environmental concerns. Specifically, it pays close attention to the inherent connections between the developing perceptions of metropolises and the various protagonists’ processes of identity formation. Beginning with Berlin, as portrayed in Andreas Steinhöfel’s popular Rico-und-Oskar-trilogy, my analysis explores the protagonist’s shifting perceptions of his urban surroundings while overcoming the limitations of his learning disability. Furthermore, my research shows how Steinhöfel’s Der mechanische Prinz utilizes the cityscape as a mirror for the protagonist’s psyche and how his knowledge of the Berlin subway system contributes to his self-healing. In contrast, China Miéville’s Un Lun Dun explores the ecocritical implications of London’s urban ‘other’, UnLondon. Katherine Marsh’s The Night Tourist and its sequel The Twilight Tourist illustrate the literary construction of New York City as a ‘mediascape’ and modern myth. Finally, I focus on alternative family structures and their connection to urban spaces, specifically Venice in Cornelia Funke’s Herr der Diebe, and Paris in Brian Selznick’s The Invention of Hugo Cabret. The study concludes with an examination of these novels’ filmic versions and their respective romanticisations of Venice and Paris. The dissertation contributes to the fields of child and youth studies and urban geographies by exposing the manifold symbiotic constructions of cityscapes and youth in post-2000 children’s and YA fiction that shape identities and spaces alike. / Thesis (Ph.D, German) -- Queen's University, 2012-09-02 16:56:36.507

This dissertation offers a new reading of Jan van der Heyden’s art through an analysis of his depiction of bricks. The minuteness with which the artist rendered bricks is frequently mentioned, their significance however, both in relation to his art and in relation to the early modern Dutch city has largely gone unnoticed. This in-depth study of Van der Heyden’s work and methods not only enriches our understanding of his paintings, but also stresses that his practice as an artist was fundamentally linked to his work as an inventor of technology. Both in his art and inventions Van der Heyden exhibited a profound interest in materials, experimentation, and the urban fabric. Additionally, this study has implications for our interpretation of the Dutch seventeenth-century painted cityscape more broadly. Cityscapes have rarely been examined in light of art theoretical treatises. Van der Heyden’s art, as well as the earliest known commentaries on his paintings however, underscore that such lack of interest is unfounded. Van der Heyden’s bricks, it will appear throughout these chapters, are an embodiment of his broad interests and wide-raging skills. They give expression to both theory and practice, as well to the formation of a Dutch identity. In Van der Heyden’s bricks, therefore, we recognize his interest in and contributions to art, technology, and the city.

In Murakami's Norwegian Wood, romance and coming-of-age confront the growing trend of postmodernity that leads to a discontinuity of life becoming more and more common in post-war Japan. As the narrator struggles through a monotonous daily existence, the text gives the reader access to the narrator's struggle for self- and societal identity. In the end, he finds his means of self-acceptance through escape, and his escape is a product of his attempts at negotiating the multiple settings or "scapes" in which he finds himself. The thesis follows the narrator through his navigation of these scapes and seeks to examine the different way that each of these scapes enables him to attempt to negotiate his role in an indifferent and increasingly consumerist society. The Introduction discusses my overview of the project, gives specifics about Murakami's life and critical reception and outlines my particular methodology. In the overview section, I address the cultural and societal tensions and changes that have occurred since the Second World War. Following this section, I provide a brief critical history of Murakami's texts, displaying not only his popularity, but also the multiple disagreements that arise over the Japanese-ness of his work. In my methodology section, I plot my eco-critical, eco-feminist, eco-psychological and deconstructive procedure for dissecting Murakami's text. The subsequent chapters perform a close reading of Murakami's text, outlining the different scapes and their attempts at establishing identity. Within these chapters, I have utilized subheadings as I felt they were needed to mark a change not on theme, but on character and emphasis. My conclusion reasserts my initial argument and further establishes the multiscapes as crucial negotiations, the price and product of which is self-identity.

Segmenting image into multiple meaningful regions is an essential task in Computer Vision. Deep Learning has been highly successful for segmentation, benefiting from the availability of the annotated datasets and deep neural network architectures. However, depth-based hand segmentation, an important application area of semantic segmentation, has yet to benefit from rich and large datasets. In addition, while deep methods provide robust solutions, they are often not efficient enough for low-powered devices. In this thesis, we focus on these two problems. To tackle the problem of lack of rich data, we propose an automatic method for generating high-quality annotations and introduce a large scale hand segmentation dataset. By exploiting the visual cues given by an RGBD sensor and a pair of colored gloves, we automatically generate dense annotations for two-hand segmentation. Our automatic annotation method lowers the cost/complexity of creating high-quality datasets and makes it easy to expand the dataset in the future. To reduce the computational requirement and allow real-time segmentation on low power devices, we propose a new representation and architecture for deep networks that predict segmentation maps based on Voronoi Diagrams. Voronoi Diagrams split space into discrete regions based on proximity to a set of points making them a powerful representation of regions, which we can then use to represent our segmentation outcomes. Specifically, we propose to estimate the location and class for these sets of points, which are then rasterized into an image. Notably, we use a differentiable definition of the Voronoi Diagram based on the softmax operator, enabling its use as a decoder layer in an end-to-end trainable network. As rasterization can take place at any given resolution, our method especially excels at rendering high-resolution segmentation maps, given a low-resolution image. We believe that our new HandSeg dataset will open new frontiers in Hand Segmentation research, and our cost-effective automatic annotation pipeline can benefit other relevant labeling tasks. Our newly proposed segmentation network enables high-quality segmentation representations that are not practically possible on low power devices using existing approaches. / Graduate

Reducing the size of a neural network whilst maintaining a comparable performance is an important problem to be solved since the constrictions on resources of small devices make it impossible to deploy large models in numerous real-life scenarios. A prominent example is autonomous driving, where computer vision tasks such as object detection and semantic segmentation need to be performed in real time by mobile devices. In this thesis, the knowledge and spherical knowledge distillation techniques are utilized to train a small model (PSPNet50) under the supervision of a large model (PSPNet101) in order to perform semantic segmentation of urban scenes. The importance of the distillation hyperparameters is studied first, namely the influence of the temperature and the weights of the loss function on the performance of the distilled model, showing no decisive advantage over the individual training of the student. Thereafter, distillation is performed utilizing a pretrained student, revealing a good improvement in performance. Contrary to expectations, the pretrained student benefits from a high learning rate when training resumes under distillation, especially in the spherical knowledge distillation case, displaying a superior and more stable performance when compared to the regular knowledge distillation setting. These findings are validated by several experiments conducted using the Cityscapes dataset. The best distilled model achieves 87.287% pixel accuracy and a 42.0% mean Intersection-Over-Union value (mIoU) on the validation set, higher than the 86.356% pixel accuracy and 39.6% mIoU obtained by the baseline student. On the test set, the official evaluation obtained by submission to the Cityscapes website yields 42.213% mIoU for the distilled model and 41.085% for the baseline student. / Att minska storleken på ett neuralt nätverk med bibehållen prestanda är ett viktigt problem som måste lösas, eftersom de begränsade resurserna i små enheter gör det omöjligt att använda stora modeller i många verkliga situationer. Ett framträdande exempel är autonom körning, där datorseende uppgifter som objektsdetektering och semantisk segmentering måste utföras i realtid av mobila enheter. I den här avhandlingen används tekniker för destillation av kunskap och sfärisk kunskap för att träna en liten modell (PSPNet50) under övervakning av en stor modell (PSPNet101) för att utföra semantisk segmentering av stadsscener. Betydelsen av hyperparametrarna för destillation studeras först, nämligen temperaturens och förlustfunktionens vikter för den destillerade modellens prestanda, vilket inte visar någon avgörande fördel jämfört med individuell träning av eleven. Därefter utförs destillation med hjälp av en utbildad elev, vilket visar på en god förbättring av prestanda. Tvärtemot förväntningarna har den utbildade eleven en hög inlärningshastighet när utbildningen återupptas under destillation, särskilt i fallet med sfärisk kunskapsdestillation, vilket ger en överlägsen och stabilare prestanda jämfört med den vanliga kunskapsdestillationssituationen. Dessa resultat bekräftas av flera experiment som utförts med hjälp av datasetet Cityscapes. Den bästa destillerade modellen uppnår 87.287% pixelprecision och ett 42.0% medelvärde för skärning över union (mIoU) på valideringsuppsättningen, vilket är högre än de 86.356% pixelprecision och 39.6% mIoU som uppnåddes av grundstudenten. I testuppsättningen ger den officiella utvärderingen som gjordes på webbplatsen Cityscapes 42.213% mIoU för den destillerade modellen och 41.085% för grundstudenten.

The objective of this master’s thesis work is to evaluate the potential benefit of a superpixel preprocessing step for general object detection in a traffic environment. The various effects of different superpixel parameters on object detection performance, as well as the benefit of including depth information when generating the superpixels are investigated. In this work, three superpixel algorithms are implemented and compared, including a proposal for an improved version of the popular Spectral Linear Iterative Clustering superpixel algorithm (SLIC). The proposed improved algorithm utilises a coarse-to-fine approach which outperforms the original SLIC for high-resolution images. An object detection algorithm is also implemented and evaluated. The algorithm makes use of depth information obtained by a stereo camera to extract superpixels corresponding to foreground objects in the image. Hierarchical clustering is then applied, with the segments formed by the clustered superpixels indicating potential objects in the input image. The object detection algorithm managed to detect on average 58% of the objects present in the chosen dataset. It performed especially well for detecting pedestrians or other objects close to the car. Altering the density distribution of the superpixels in the image yielded an increase in detection rate, and could be achieved both with or without utilising depth information. It was also shown that the use of superpixels greatly reduces the amount of computations needed for the algorithm, indicating that a real-time implementation is feasible.

This paper deals with classifying objects using deep neural networks. Whole scene segmentation was used as main algorithm for the classification purpose which works with video sequences and obtains information between two video frames. Optical flow was used for getting information from the video frames, based on which features maps of a~neural network are warped. Two neural network architectures were adjusted to work with videos and experimented with. Results of the experiments show, that using videos for image segmentation improves accuracy (IoU) compared to the same architecture working with images.

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Deep learning for safe autonomous transport is rapidly emerging. Fast and robust perception for autonomous vehicles will be crucial for future navigation in urban areas with high traffic and human interplay. Previous work focuses on extracting full image depth maps, or finding specific road features such as lanes. However, in urban environments lanes are not always present, and sensors such as LiDAR with 3D point clouds provide a quite sparse depth perception of road with demanding algorithmic approaches. In this thesis we derive a novel convolutional neural network that we call AutoNet. It is designed as an encoder-decoder network for pixel-wise depth estimation of an urban drivable free-space road, using only a monocular camera, and handled as a supervised regression problem. AutoNet is also constructed as a classification network to solely classify and segment the drivable free-space in real- time with monocular vision, handled as a supervised classification problem, which shows to be a simpler and more robust solution than the regression approach. We also implement the state of the art neural network ENet for comparison, which is designed for fast real-time semantic segmentation and fast inference speed. The evaluation shows that AutoNet outperforms ENet for every performance metrics, but shows to be slower in terms of frame rate. However, optimization techniques are proposed for future work, on how to advance the frame rate of the network while still maintaining the robustness and performance. All the training and evaluation is done on the Cityscapes dataset. New ground truth labels for road depth perception are created for training with a novel approach of fusing pre-computed depth maps with semantic labels. Data collection with a Scania vehicle is conducted, mounted with a monocular camera to test the final derived models. The proposed AutoNet shows promising state of the art performance in regards to road depth estimation as well as road classification. / Deep learning för säkra autonoma transportsystem framträder mer och mer inom forskning och utveckling. Snabb och robust uppfattning om miljön för autonoma fordon kommer att vara avgörande för framtida navigering inom stadsområden med stor trafiksampel. I denna avhandling härleder vi en ny form av ett neuralt nätverk som vi kallar AutoNet. Där nätverket är designat som en autoencoder för pixelvis djupskattning av den fria körbara vägytan för stadsområden, där nätverket endast använder sig av en monokulär kamera och dess bilder. Det föreslagna nätverket för djupskattning hanteras som ett regressions problem. AutoNet är även konstruerad som ett klassificeringsnätverk som endast ska klassificera och segmentera den körbara vägytan i realtid med monokulärt seende. Där detta är hanterat som ett övervakande klassificerings problem, som även visar sig vara en mer simpel och mer robust lösning för att hitta vägyta i stadsområden. Vi implementerar även ett av de främsta neurala nätverken ENet för jämförelse. ENet är utformat för snabb semantisk segmentering i realtid, med hög prediktions- hastighet. Evalueringen av nätverken visar att AutoNet utklassar ENet i varje prestandamätning för noggrannhet, men visar sig vara långsammare med avseende på antal bilder per sekund. Olika optimeringslösningar föreslås för framtida arbete, för hur man ökar nätverk-modelens bildhastighet samtidigt som man behåller robustheten.All träning och utvärdering görs på Cityscapes dataset. Ny data för träning samt evaluering för djupskattningen för väg skapas med ett nytt tillvägagångssätt, genom att kombinera förberäknade djupkartor med semantiska etiketter för väg. Datainsamling med ett Scania-fordon utförs även, monterad med en monoculär kamera för att testa den slutgiltiga härleda modellen. Det föreslagna nätverket AutoNet visar sig vara en lovande topp-presterande modell i fråga om djupuppskattning för väg samt vägklassificering för stadsområden.

In der vorliegenden Arbeit wird die Bedeutung der Landschafts- und Marinemalerei für die nationale Einheit und bürgerliche Identifikation im Kaiserreich und in der Weimarer Republik beispielhaft am Werk des Berliner Secessionskünstlers Ulrich Hübner (1872-1932) untersucht. Zu Beginn wird die Rolle der Landschaft für die Konstitution von Staatswesen und nationaler Identität allgemein erläutert, um deutlich zu machen, vor welchem Hintergrund die Landschaftsmalerei Ulrich Hübners einen Beitrag zur bürgerlich-nationalen Identitätsfindung im (neugegründeten) Nationalstaat leisten konnte. Im Hauptteil werden dazu anhand Hübners biographischer Stationen unterschiedliche Gesichtspunkte, wie die pluralistischen Einflüsse auf Hübners Landschaftsmalerei, seine Rolle in der Berliner Secession, seine Positionierung auf dem Kunstmarkt, seine Rezeption durch die Kunstkritik und seine Funktion an der Akademie der Künste, untersucht. Hübners Konzentration auf Küstendarstellungen, Seestücke und Stadtansichten führte in Abgrenzung zur Marinemalerei zu dem neuen Typus der Stadt- und Wasserlandschaft, zwischen klassischer Veduten- und Landschaftsmalerei und impressionistischen Stimmungsbildern. Auf Grundlage des erstellten Werkverzeichnisses wird durch die Betrachtung Hübners Werks im Gesamtzusammenhang des Berliner Kunstgeschehens unter dem Aspekt des wirtschaftlichen und gesellschaftlichen Erfolges und der Entwicklung des privatwirtschaftlichen Kunstmarktes deutlich, wie ein Künstler in diesem System agierte. Hübners Erfolg mit dem neuen Typus der Stadtlandschaft und der Konzentration auf bestimmte Vertriebswege und erfolgreiche Motive steht exemplarisch für den deutschen Impressionismus in Zeiten des Stilpluralismus. Als Vertreter einer moderaten Moderne wurden seine Gemälde heimatlicher Landschaften Identifikationsbilder des aufgeschlossenen Bürgertums und somit eine Versicherung von Kontinuität in den politisch bewegten Zeiten vom Kaiserreich bis zum Ende der Weimarer Republik. / This doctoral thesis examines the significance of landscape and maritime painting for the national unity and civil identification in Imperial Germany and the Weimar Republic through the case study of the oeuvre of the Berlin Secession artist Ulrich Hübner (1872-1932). In the first instance, we will outline the effect that landscape painting in general had on the constitution of the political system and national identity in order to assess the extent of which Hübner’s landscape painting contributed towards the shaping of a civil-national identity in the newly founded nation state. To this effect we will then study key events in his biography, focusing on the following aspects: the pluralist influences that shape Hübner’s landscape painting, his role in the Berlin Secession, his place in the art market, art criticism’s response to his work and his position at the Berlin Academy of Arts. Hübner’s focus on coastal views, sea- and cityscapes, as opposed to maritime painting, lead to the new type of Urban Landscape and Waterscape which is situated between classical Veduta and landscape painting on one hand and impressionist “Stimmungsbilder” on the other. The catalogue raisonné will form the basis on which we examine his oeuvre in the context of the greater Berlin art scene with particular emphasis on his commercial and social success on one hand as well as seen within the more specific framework which is the development of the commercial art market on the other. The success Hübner had with his new type of Urban Landscape and his focus on specific commercial channels and successful subject matters is exemplary for the German Impressionism in times of stylistic pluralism. Representing a moderate Modernism, his paintings of “Heimat”-landscapes became symbols that the liberal bourgeoisie could identify with and thereby a guarantee of continuity during the politically agitated times from the beginning of the German Empire to the end of the Weimar Republic.