16 research outputs found
State of the Art on Diffusion Models for Visual Computing
The field of visual computing is rapidly advancing due to the emergence of
generative artificial intelligence (AI), which unlocks unprecedented
capabilities for the generation, editing, and reconstruction of images, videos,
and 3D scenes. In these domains, diffusion models are the generative AI
architecture of choice. Within the last year alone, the literature on
diffusion-based tools and applications has seen exponential growth and relevant
papers are published across the computer graphics, computer vision, and AI
communities with new works appearing daily on arXiv. This rapid growth of the
field makes it difficult to keep up with all recent developments. The goal of
this state-of-the-art report (STAR) is to introduce the basic mathematical
concepts of diffusion models, implementation details and design choices of the
popular Stable Diffusion model, as well as overview important aspects of these
generative AI tools, including personalization, conditioning, inversion, among
others. Moreover, we give a comprehensive overview of the rapidly growing
literature on diffusion-based generation and editing, categorized by the type
of generated medium, including 2D images, videos, 3D objects, locomotion, and
4D scenes. Finally, we discuss available datasets, metrics, open challenges,
and social implications. This STAR provides an intuitive starting point to
explore this exciting topic for researchers, artists, and practitioners alike
Harvesting the Interactive Potential of Digital Displays in Public Space: The Poetics of Public Interaction
A digital public display is a platform of media architecture that can either take the form of a large-size stand-alone screen, which relies on LED, LCD or plasma technology, or else a video projection that illuminate the façades of buildings in dark settings. Like nondigital advertising billboards since the nineteenth-century, digital public displays typically tend to be used to deliver commercial content, publicize news and offer context-relevant information in accordance with the elementary one-way transmission model of communication. As a result, until recently, most public media displays remained non-interactive. But now that computational systems can support digitally-mediated interactions on this platform, interactive screen technology is becoming an increasingly common component of new urban digital infrastructures in semi-public and public space. This doctoral research examines how the interactive potential of digital public displays might be unleashed at the scale of the built environment if designers were to focus on their public vocation and their social affordances. In the past decade, display-based systems have mostly been studied, designed and produced top-down style by experts. However, some researchers have called for new methodologies that could help effectively bridge the gap between the top-down prescriptive design approaches and the bottom-up appropriative digital practices that shape the in situ usages of this urban technology. This doctoral work strives to take up this challenge by demonstrating that multisited design is an approach that can be used to shape the conception and function of interactive digital public displays in the context of urban infrastructural planning. An interpretive outcome of participant observation, this dissertation also reports on field observations made over two years, presented as a narrative punctuated with micro-analyses on design research. This further contributes to the literature by, first, implicitly suggesting throughout that the concept of real time public interaction can provide an abstraction that facilitates thinking about the design of interactive digital public displays; second, presenting thick descriptions that evoke four new possible purposes for this platform; and third, developing the concept of social affordances tailored to public space
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Proceedings of EVA London 2024
The Electronic Visualisation and the Arts London 2024 Conference (EVA London 2024) is co-sponsored by the Computer Arts Society (CAS) and BCS, the Chartered Institute for IT, of which the CAS is a Specialist Group. As for 2022, the EVA London 2023 Conference is a physical and online âhybridâ conference. We continue with publishing the proceedings, both online, with open access via ScienceOpen, and also in our traditional printed form, in full colour. The main conference presentations run during 10â13 July 2023, with workshops and other activities, especially for students, on 14 July 2023
Sustainable Smart Cities and Smart Villages Research
ca. 200 words; this text will present the book in all promotional forms (e.g. flyers). Please describe the book in straightforward and consumer-friendly terms. [There is ever more research on smart cities and new interdisciplinary approaches proposed on the study of smart cities. At the same time, problems pertinent to communities inhabiting rural areas are being addressed, as part of discussions in contigious fields of research, be it environmental studies, sociology, or agriculture. Even if rural areas and countryside communities have previously been a subject of concern for robust policy frameworks, such as the European Unionâs Cohesion Policy and Common Agricultural Policy Arguably, the concept of âthe villageâ has been largely absent in the debate. As a result, when advances in sophisticated information and communication technology (ICT) led to the emergence of a rich body of research on smart cities, the application and usability of ICT in the context of a village has remained underdiscussed in the literature. Against this backdrop, this volume delivers on four objectives. It delineates the conceptual boundaries of the concept of âsmart villageâ. It highlights in which ways âsmart villageâ is distinct from âsmart cityâ. It examines in which ways smart cities research can enrich smart villages research. It sheds light on the smart village research agenda as it unfolds in European and global contexts.
Deep learning for medical image processing
Medical image segmentation represents a fundamental aspect of medical image computing. It facilitates measurements of anatomical structures, like organ volume and tissue thickness, critical for many classification algorithms which can be instrumental for clinical diagnosis. Consequently, enhancing the efficiency and accuracy of segmentation algorithms could lead to considerable improvements in patient care and diagnostic precision.
In recent years, deep learning has become the state-of-the-art approach in various domains of medical image computing, including medical image segmentation.
The key advantages of deep learning methods are their speed and efficiency, which have the potential to transform clinical practice significantly. Traditional algorithms might require hours to perform complex computations, but with deep learning, such computational tasks can be executed much faster, often within seconds.
This thesis focuses on two distinct segmentation strategies: voxel-based and surface-based.
Voxel-based segmentation assigns a class label to each individual voxel of an image. On the other hand, surface-based segmentation techniques involve reconstructing a 3D surface from the input images, then segmenting that surface into different regions.
This thesis presents multiple methods for voxel-based image segmentation. Here, the focus is segmenting brain structures, white matter hyperintensities, and abdominal organs. Our approaches confront challenges such as domain adaptation, learning with limited data, and optimizing network architectures to handle 3D images. Additionally, the thesis discusses ways to handle the failure cases of standard deep learning approaches, such as dealing with rare cases like patients who have undergone organ resection surgery.
Finally, the thesis turns its attention to cortical surface reconstruction and parcellation. Here, deep learning is used to extract cortical surfaces from MRI scans as triangular meshes and parcellate these surfaces on a vertex level. The challenges posed by this approach include handling irregular and topologically complex structures.
This thesis presents novel deep learning strategies for voxel-based and surface-based medical image segmentation. By addressing specific challenges in each approach, it aims to contribute to the ongoing advancement of medical image computing.Die Segmentierung medizinischer Bilder stellt einen fundamentalen Aspekt der medizinischen Bildverarbeitung dar. Sie erleichtert Messungen anatomischer Strukturen, wie Organvolumen und Gewebedicke, die fĂŒr viele Klassifikationsalgorithmen entscheidend sein können und somit fĂŒr klinische Diagnosen von Bedeutung sind. Daher könnten Verbesserungen in der Effizienz und Genauigkeit von Segmentierungsalgorithmen zu erheblichen Fortschritten in der Patientenversorgung und diagnostischen Genauigkeit fĂŒhren.
Deep Learning hat sich in den letzten Jahren als fĂŒhrender Ansatz in verschiedenen Be-reichen der medizinischen Bildverarbeitung etabliert. Die Hauptvorteile dieser Methoden sind Geschwindigkeit und Effizienz, die die klinische Praxis erheblich verĂ€ndern können. Traditionelle Algorithmen benötigen möglicherweise Stunden, um komplexe Berechnungen durchzufĂŒhren, mit Deep Learning können solche rechenintensiven Aufgaben wesentlich schneller, oft innerhalb von Sekunden, ausgefĂŒhrt werden.
Diese Dissertation konzentriert sich auf zwei Segmentierungsstrategien, die voxel- und oberflÀchenbasierte Segmentierung. Die voxelbasierte Segmentierung weist jedem Voxel eines Bildes ein Klassenlabel zu, wÀhrend oberflÀchenbasierte Techniken eine 3D-OberflÀche aus den Eingabebildern rekonstruieren und segmentieren.
In dieser Arbeit werden mehrere Methoden fĂŒr die voxelbasierte Bildsegmentierung vorgestellt. Der Fokus liegt hier auf der Segmentierung von Gehirnstrukturen, HyperintensitĂ€ten der weiĂen Substanz und abdominellen Organen. Unsere AnsĂ€tze begegnen Herausforderungen wie der Anpassung an verschiedene DomĂ€nen, dem Lernen mit begrenzten Daten und der Optimierung von Netzwerkarchitekturen, um 3D-Bilder zu verarbeiten. DarĂŒber hinaus werden in dieser Dissertation Möglichkeiten erörtert, mit den FehlschlĂ€gen standardmĂ€Ăiger Deep-Learning-AnsĂ€tze umzugehen, beispielsweise mit seltenen FĂ€llen nach einer Organresektion.
SchlieĂlich legen wir den Fokus auf die Rekonstruktion und Parzellierung von kortikalen OberflĂ€chen. Hier wird Deep Learning verwendet, um kortikale OberflĂ€chen aus MRT-Scans als Dreiecksnetz zu extrahieren und diese OberflĂ€chen auf Knoten-Ebene zu parzellieren. Zu den Herausforderungen dieses Ansatzes gehört der Umgang mit unregelmĂ€Ăigen und topologisch komplexen Strukturen.
Diese Arbeit stellt neuartige Deep-Learning-Strategien fĂŒr die voxel- und oberflĂ€chenbasierte medizinische Segmentierung vor. Durch die BewĂ€ltigung spezifischer Herausforderungen in jedem Ansatz trĂ€gt sie so zur Weiterentwicklung der medizinischen Bildverarbeitung bei
Divergence in Architectural Research
ConCave Ph.D. Symposium 2020: Divergence in Architectural Research, March 5-6, 2020, Georgia Institute of Technology, Atlanta, GA.The essays in this volume have come together under the theme âDivergence in Architectural Researchâ and present a snapshot of Ph.D. research being conducted in over thirty architectural research institutions, representing fourteen countries around the world. These essays also provide a window into the presentations and discussions that took place March 5-6, 2020, during the ConCave Ph.D. Symposium âDivergence in Architectural Research,â under the auspices of the School of Architecture, Georgia Institute of Technology, in Atlanta, Georgia.
On a preliminary reading, the essays respond to the call of divergence by doing just that; they present the great diversity of research topics, methodologies, and practices currently found under the umbrella of âarchitectural research.â They inform inquiry within architectural programs and across disciplinary concentrations, and also point to the ways that the academy, research methodologies, and the design profession are evolving and encroaching upon one another, with the unspoken hope of encouraging new relationships, reconfiguring previous assumptions about the discipline, and interweaving research and practice