4,307 research outputs found
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Ontologies and representation spaces for sketch map interpretation
In this paper, we present a systematic approach to sketch map interpretation. The method decomposes the elements of a sketch map into a hierarchy of categories, from the material sketch map level to the non-material representational sketch map level, and then interprets the sketch map using the five formal representation spaces that we develop. These spaces (set, graph, metric and Euclidean) provide a tiered formal representation based on standard mathematical structures. We take the view that a sketch map bears information about the physical world and systematises this using extensions of existing formal ontologies. The motivation for this work is the partially automatic extraction and integration of information from sketch maps. We propose a set of ontologies and methods as a first step in the direction of a formalisation of partially automatic extraction and integration of sketch map content. We also see this work as a contribution to spatial cognition, where researchers externalise spatial knowledge using sketch mapping. The paper concludes by working through an example that demonstrates the sketch map interpretation at different levels using the underlying method
Improving offline evaluation of contextual bandit algorithms via bootstrapping techniques
In many recommendation applications such as news recommendation, the items
that can be rec- ommended come and go at a very fast pace. This is a challenge
for recommender systems (RS) to face this setting. Online learning algorithms
seem to be the most straight forward solution. The contextual bandit framework
was introduced for that very purpose. In general the evaluation of a RS is a
critical issue. Live evaluation is of- ten avoided due to the potential loss of
revenue, hence the need for offline evaluation methods. Two options are
available. Model based meth- ods are biased by nature and are thus difficult to
trust when used alone. Data driven methods are therefore what we consider here.
Evaluat- ing online learning algorithms with past data is not simple but some
methods exist in the litera- ture. Nonetheless their accuracy is not satisfac-
tory mainly due to their mechanism of data re- jection that only allow the
exploitation of a small fraction of the data. We precisely address this issue
in this paper. After highlighting the limita- tions of the previous methods, we
present a new method, based on bootstrapping techniques. This new method comes
with two important improve- ments: it is much more accurate and it provides a
measure of quality of its estimation. The latter is a highly desirable property
in order to minimize the risks entailed by putting online a RS for the first
time. We provide both theoretical and ex- perimental proofs of its superiority
compared to state-of-the-art methods, as well as an analysis of the convergence
of the measure of quality
Deep Learning for Free-Hand Sketch: A Survey
Free-hand sketches are highly illustrative, and have been widely used by
humans to depict objects or stories from ancient times to the present. The
recent prevalence of touchscreen devices has made sketch creation a much easier
task than ever and consequently made sketch-oriented applications increasingly
popular. The progress of deep learning has immensely benefited free-hand sketch
research and applications. This paper presents a comprehensive survey of the
deep learning techniques oriented at free-hand sketch data, and the
applications that they enable. The main contents of this survey include: (i) A
discussion of the intrinsic traits and unique challenges of free-hand sketch,
to highlight the essential differences between sketch data and other data
modalities, e.g., natural photos. (ii) A review of the developments of
free-hand sketch research in the deep learning era, by surveying existing
datasets, research topics, and the state-of-the-art methods through a detailed
taxonomy and experimental evaluation. (iii) Promotion of future work via a
discussion of bottlenecks, open problems, and potential research directions for
the community.Comment: This paper is accepted by IEEE TPAM
Towards Practicality of Sketch-Based Visual Understanding
Sketches have been used to conceptualise and depict visual objects from
pre-historic times. Sketch research has flourished in the past decade,
particularly with the proliferation of touchscreen devices. Much of the
utilisation of sketch has been anchored around the fact that it can be used to
delineate visual concepts universally irrespective of age, race, language, or
demography. The fine-grained interactive nature of sketches facilitates the
application of sketches to various visual understanding tasks, like image
retrieval, image-generation or editing, segmentation, 3D-shape modelling etc.
However, sketches are highly abstract and subjective based on the perception of
individuals. Although most agree that sketches provide fine-grained control to
the user to depict a visual object, many consider sketching a tedious process
due to their limited sketching skills compared to other query/support
modalities like text/tags. Furthermore, collecting fine-grained sketch-photo
association is a significant bottleneck to commercialising sketch applications.
Therefore, this thesis aims to progress sketch-based visual understanding
towards more practicality.Comment: PhD thesis successfully defended by Ayan Kumar Bhunia, Supervisor:
Prof. Yi-Zhe Song, Thesis Examiners: Prof Stella Yu and Prof Adrian Hilto
Mobile Wound Assessment and 3D Modeling from a Single Image
The prevalence of camera-enabled mobile phones have made mobile wound assessment a viable treatment option for millions of previously difficult to reach patients. We have designed a complete mobile wound assessment platform to ameliorate the many challenges related to chronic wound care. Chronic wounds and infections are the most severe, costly and fatal types of wounds, placing them at the center of mobile wound assessment. Wound physicians assess thousands of single-view wound images from all over the world, and it may be difficult to determine the location of the wound on the body, for example, if the wound is taken at close range. In our solution, end-users capture an image of the wound by taking a picture with their mobile camera. The wound image is segmented and classified using modern convolution neural networks, and is stored securely in the cloud for remote tracking. We use an interactive semi-automated approach to allow users to specify the location of the wound on the body. To accomplish this we have created, to the best our knowledge, the first 3D human surface anatomy labeling system, based off the current NYU and Anatomy Mapper labeling systems. To interactively view wounds in 3D, we have presented an efficient projective texture mapping algorithm for texturing wounds onto a 3D human anatomy model. In so doing, we have demonstrated an approach to 3D wound reconstruction that works even for a single wound image
3D terrain generation using neural networks
With the increase in computation power, coupled with the advancements in the field in the form of
GANs and cGANs, Neural Networks have become an attractive proposition for content generation. This
opened opportunities for Procedural Content Generation algorithms (PCG) to tap Neural Networks
generative power to create tools that allow developers to remove part of creative and developmental
burden imposed throughout the gaming industry, be it from investors looking for a return on their
investment and from consumers that want more and better content, fast. This dissertation sets out to
develop a PCG mixed-initiative tool, leveraging cGANs, to create authored 3D terrains, allowing users
to directly influence the resulting generated content without the need for formal training on terrain
generation or complex interactions with the tool to influence the generative output, as opposed to
state of the art generative algorithms that only allow for random content generation or are needlessly
complex. Testing done to 113 people online, as well as in-person testing done to 30 people, revealed
that it is indeed possible to develop a tool that allows users from any level of terrain creation
knowledge, and minimal tool training, to easily create a 3D terrain that is more realistic looking than
those generated by state-of-the-art solutions such as Perlin Noise.Com o aumento do poder de computação, juntamente com os avanços neste campo na forma de GANs
e cGANs, as Redes Neurais tornaram-se numa proposta atrativa para a geração de conteúdos. Graças
a estes avanços, abriram-se oportunidades para os algoritmos de Geração de Conteúdos
Procedimentais(PCG) explorarem o poder generativo das Redes Neurais para a criação de ferramentas
que permitam aos programadores remover parte da carga criativa e de desenvolvimento imposta em
toda a indústria dos jogos, seja por parte dos investidores que procuram um retorno do seu
investimento ou por parte dos consumidores que querem mais e melhor conteúdo, o mais rápido
possível. Esta dissertação pretende desenvolver uma ferramenta de iniciativa mista PCG, alavancando
cGANs, para criar terrenos 3D cocriados, permitindo aos utilizadores influenciarem diretamente o
conteúdo gerado sem necessidade de terem formação formal sobre a criação de terrenos 3D ou
interações complexas com a ferramenta para influenciar a produção generativa, opondo-se assim a
algoritmos generativos comummente utilizados, que apenas permitem a geração de conteúdo
aleatório ou que são desnecessariamente complexos. Um conjunto de testes feitos a 113 pessoas
online e a 30 pessoas presencialmente, revelaram que é de facto possível desenvolver uma ferramenta
que permita aos utilizadores, de qualquer nível de conhecimento sobre criação de terrenos, e com
uma formação mínima na ferramenta, criar um terreno 3D mais realista do que os terrenos gerados a
partir da solução de estado da arte, como o Perlin Noise, e de uma forma fácil
HARPS: An Online POMDP Framework for Human-Assisted Robotic Planning and Sensing
Autonomous robots can benefit greatly from human-provided semantic
characterizations of uncertain task environments and states. However, the
development of integrated strategies which let robots model, communicate, and
act on such 'soft data' remains challenging. Here, the Human Assisted Robotic
Planning and Sensing (HARPS) framework is presented for active semantic sensing
and planning in human-robot teams to address these gaps by formally combining
the benefits of online sampling-based POMDP policies, multimodal semantic
interaction, and Bayesian data fusion. This approach lets humans
opportunistically impose model structure and extend the range of semantic soft
data in uncertain environments by sketching and labeling arbitrary landmarks
across the environment. Dynamic updating of the environment model while during
search allows robotic agents to actively query humans for novel and relevant
semantic data, thereby improving beliefs of unknown environments and states for
improved online planning. Simulations of a UAV-enabled target search
application in a large-scale partially structured environment show significant
improvements in time and belief state estimates required for interception
versus conventional planning based solely on robotic sensing. Human subject
studies in the same environment (n = 36) demonstrate an average doubling in
dynamic target capture rate compared to the lone robot case, and highlight the
robustness of active probabilistic reasoning and semantic sensing over a range
of user characteristics and interaction modalities
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Style-driven Shape Analysis and Synthesis
In this dissertation I will investigate algorithms that analyze stylistic properties of 3D shapes and automatically synthesize shapes given style specifications. I will start by introducing a structure-transcending method for style similarity evaluation between 3D shapes. Inspired by observations about style similarity in art history literature, we propose an algorithmically computed style similarity measure which identifies style related elements on the analyzed models and collates element-level geometric similarity measurements into an object-level style measure consistent with human perception. To achieve this consistency we employ crowdsourcing to learn the relative perceptual importance of a range of elementary shape distances and other parameters used in our measurement from participant answers to cross-structure style similarity queries. I will then describe an algorithm that utilizes this learned style similarity measure to synthesize 3D models of man-made shapes. The algorithm combines user-specified style, described via an exemplar shape, and functionality, encoded by a functionally different target shape. We transfer the exemplar style to the target via a sequence of compatible element-level operations where the compatibility is a learned metric that estimates the impact of each operation on the edited shape. We use this metric to cast style transfer as a tabu search, which incrementally updates the target shape using compatible operations, progressively increasing its style similarity to the exemplar while strictly maintaining its functionality at each step. Finally I will propose a method for reconstructing 3D shapes following style aspects of given 2D drawings. Our method takes line drawings as input and converts them into surface depth and normal maps from several output viewpoints via a deep convolutional neural network with multi-view encoder-decoder architecture. The multi-view maps are then consolidated into a dense coherent 3D point cloud by solving an optimization problem that fuses depth and normal information across all output viewpoints. The output point cloud is then converted into a polygon mesh representation, which is further fine-tuned to match the input sketch more precisely
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