3,086 research outputs found
Monoidal computer III: A coalgebraic view of computability and complexity
Monoidal computer is a categorical model of intensional computation, where
many different programs correspond to the same input-output behavior. The
upshot of yet another model of computation is that a categorical formalism
should provide a much needed high level language for theory of computation,
flexible enough to allow abstracting away the low level implementation details
when they are irrelevant, or taking them into account when they are genuinely
needed. A salient feature of the approach through monoidal categories is the
formal graphical language of string diagrams, which supports visual reasoning
about programs and computations.
In the present paper, we provide a coalgebraic characterization of monoidal
computer. It turns out that the availability of interpreters and specializers,
that make a monoidal category into a monoidal computer, is equivalent with the
existence of a *universal state space*, that carries a weakly final state
machine for any pair of input and output types. Being able to program state
machines in monoidal computers allows us to represent Turing machines, to
capture their execution, count their steps, as well as, e.g., the memory cells
that they use. The coalgebraic view of monoidal computer thus provides a
convenient diagrammatic language for studying computability and complexity.Comment: 34 pages, 24 figures; in this version: added the Appendi
Picasso, Matisse, or a Fake? Automated Analysis of Drawings at the Stroke Level for Attribution and Authentication
This paper proposes a computational approach for analysis of strokes in line
drawings by artists. We aim at developing an AI methodology that facilitates
attribution of drawings of unknown authors in a way that is not easy to be
deceived by forged art. The methodology used is based on quantifying the
characteristics of individual strokes in drawings. We propose a novel algorithm
for segmenting individual strokes. We designed and compared different
hand-crafted and learned features for the task of quantifying stroke
characteristics. We also propose and compare different classification methods
at the drawing level. We experimented with a dataset of 300 digitized drawings
with over 80 thousands strokes. The collection mainly consisted of drawings of
Pablo Picasso, Henry Matisse, and Egon Schiele, besides a small number of
representative works of other artists. The experiments shows that the proposed
methodology can classify individual strokes with accuracy 70%-90%, and
aggregate over drawings with accuracy above 80%, while being robust to be
deceived by fakes (with accuracy 100% for detecting fakes in most settings)
Evaluation of metal artefact reduction using dual-step adaptive thresholding technique in computed tomography
Background: Metal objects present in CT images may give rise to streak artefact. In the
presence of severe artefacts, image quality may be extensively degraded and important
clinical findings and pathology in the vicinity of the metal objects may be obscured. The
purpose of this study is to evaluate the effectiveness of the dual-step adaptive thresholding
technique as a method of metal artefact reduction in CT studies.
Methodology: A total of 14 CT studies which contained metal-induced artefacts resulted
from various surgical implants were retrieved from the Picture Archive Communication
System (PACS). The CT images were corrected using the DSAT algorithm in MATLAB
workspace to generate the artefact-corrected images with acceptable quality. Both groups
of original images and artefact-corrected images were evaluated quantitatively using
noise and SNR and qualitatively using visual evaluation by 2 evaluators. Level of
significance was determined (p < 0.05).
Results: A significant reduction of the noise were noticed in the corrected CT images
following DSAT technique for metal artefact correction with the mean noise of 14.576 ±
11.7 as compared to the original images with mean of 40.177 ± 23.785 (p < 0.0005). A
significant improvement of SNR was also demonstrated following DSAT correction with
the mean SNR of 3.877 ± 3.931 for the corrected images in comparison to 3.614 ± 2.839
for the original images (p = 0.017). Visual evaluation has demonstrated reduced
appearance of metal artefacts with increased conspicuity of adjacent structures (p < 0.05).
Conclusion: Metal artefact correction using dual-step adaptive thresholding technique
has the ability to suppress metal-induced artefacts with significant improvement of image
quality
Um modelo para suporte automatizado ao reconhecimento, extração, personalização e reconstrução de gráficos estáticos
Data charts are widely used in our daily lives, being present in regular media,
such as newspapers, magazines, web pages, books, and many others. A well constructed
data chart leads to an intuitive understanding of its underlying data
and in the same way, when data charts have wrong design choices, a redesign
of these representations might be needed. However, in most cases, these
charts are shown as a static image, which means that the original data are not
usually available. Therefore, automatic methods could be applied to extract the
underlying data from the chart images to allow these changes. The task of
recognizing charts and extracting data from them is complex, largely due to the
variety of chart types and their visual characteristics.
Computer Vision techniques for image classification and object detection are
widely used for the problem of recognizing charts, but only in images without
any disturbance. Other features in real-world images that can make this task
difficult are not present in most literature works, like photo distortions, noise,
alignment, etc. Two computer vision techniques that can assist this task and
have been little explored in this context are perspective detection and
correction. These methods transform a distorted and noisy chart in a clear
chart, with its type ready for data extraction or other uses. The task of
reconstructing data is straightforward, as long the data is available the
visualization can be reconstructed, but the scenario of reconstructing it on the
same context is complex.
Using a Visualization Grammar for this scenario is a key component, as these
grammars usually have extensions for interaction, chart layers, and multiple
views without requiring extra development effort.
This work presents a model for automated support for custom recognition, and
reconstruction of charts in images. The model automatically performs the
process steps, such as reverse engineering, turning a static chart back into its
data table for later reconstruction, while allowing the user to make modifications
in case of uncertainties. This work also features a model-based architecture
along with prototypes for various use cases. Validation is performed step by
step, with methods inspired by the literature. This work features three use
cases providing proof of concept and validation of the model.
The first use case features usage of chart recognition methods focused on
documents in the real-world, the second use case focus on vocalization of
charts, using a visualization grammar to reconstruct a chart in audio format,
and the third use case presents an Augmented Reality application that
recognizes and reconstructs charts in the same context (a piece of paper)
overlaying the new chart and interaction widgets. The results showed that with
slight changes, chart recognition and reconstruction methods are now ready for
real-world charts, when taking time, accuracy and precision into consideration.Os gráficos de dados são amplamente utilizados na nossa vida diária, estando
presentes nos meios de comunicação regulares, tais como jornais, revistas,
páginas web, livros, e muitos outros. Um gráfico bem construído leva a uma
compreensão intuitiva dos seus dados inerentes e da mesma forma, quando
os gráficos de dados têm escolhas de conceção erradas, poderá ser
necessário um redesenho destas representações. Contudo, na maioria dos
casos, estes gráficos são mostrados como uma imagem estática, o que
significa que os dados originais não estão normalmente disponíveis. Portanto,
poderiam ser aplicados métodos automáticos para extrair os dados inerentes
das imagens dos gráficos, a fim de permitir estas alterações. A tarefa de
reconhecer os gráficos e extrair dados dos mesmos é complexa, em grande
parte devido à variedade de tipos de gráficos e às suas características visuais.
As técnicas de Visão Computacional para classificação de imagens e deteção
de objetos são amplamente utilizadas para o problema de reconhecimento de
gráficos, mas apenas em imagens sem qualquer ruído. Outras características
das imagens do mundo real que podem dificultar esta tarefa não estão
presentes na maioria das obras literárias, como distorções fotográficas, ruído,
alinhamento, etc. Duas técnicas de visão computacional que podem ajudar
nesta tarefa e que têm sido pouco exploradas neste contexto são a deteção e
correção da perspetiva. Estes métodos transformam um gráfico distorcido e
ruidoso em um gráfico limpo, com o seu tipo pronto para extração de dados
ou outras utilizações. A tarefa de reconstrução de dados é simples, desde que
os dados estejam disponíveis a visualização pode ser reconstruída, mas o
cenário de reconstrução no mesmo contexto é complexo.
A utilização de uma Gramática de Visualização para este cenário é um
componente chave, uma vez que estas gramáticas têm normalmente
extensões para interação, camadas de gráficos, e visões múltiplas sem exigir
um esforço extra de desenvolvimento.
Este trabalho apresenta um modelo de suporte automatizado para o
reconhecimento personalizado, e reconstrução de gráficos em imagens
estáticas. O modelo executa automaticamente as etapas do processo, tais
como engenharia inversa, transformando um gráfico estático novamente na
sua tabela de dados para posterior reconstrução, ao mesmo tempo que
permite ao utilizador fazer modificações em caso de incertezas. Este trabalho
também apresenta uma arquitetura baseada em modelos, juntamente com
protótipos para vários casos de utilização. A validação é efetuada passo a
passo, com métodos inspirados na literatura. Este trabalho apresenta três
casos de uso, fornecendo prova de conceito e validação do modelo.
O primeiro caso de uso apresenta a utilização de métodos de reconhecimento
de gráficos focando em documentos no mundo real, o segundo caso de uso
centra-se na vocalização de gráficos, utilizando uma gramática de visualização
para reconstruir um gráfico em formato áudio, e o terceiro caso de uso
apresenta uma aplicação de Realidade Aumentada que reconhece e reconstrói
gráficos no mesmo contexto (um pedaço de papel) sobrepondo os novos
gráficos e widgets de interação. Os resultados mostraram que com pequenas
alterações, os métodos de reconhecimento e reconstrução dos gráficos estão
agora prontos para os gráficos do mundo real, tendo em consideração o
tempo, a acurácia e a precisão.Programa Doutoral em Engenharia Informátic
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