364,562 research outputs found
The Bechdel Test and the Social Form of Character Networks
This essay describes the popular Bechdel Test—a measure of women’s dialogue in films—in terms of social network analysis within fictional narrative. It argues that this form of vernacular criticism arrives at a productive convergence with contemporary academic critical methodologies in surface and postcritical reading practices, on the one hand, and digital humanities, on the other. The data-oriented character of the Bechdel Test, which a text rigidly passes or fails, stands in sharp contrast to identification- or recognition-based evaluations of a text’s feminist orientation, particularly because the former does not prescribe the content, but merely the social form, of women’s agency. This essay connects the Bechdel Test and a lineage of feminist and early queer theory to current work on social network analysis within literary texts, and it argues that the Bechdel Test offers the beginnings of a measured approach to understanding agency within actor networks
Turbulence in the Solar Atmosphere: Manifestations and Diagnostics via Solar Image Processing
Intermittent magnetohydrodynamical turbulence is most likely at work in the
magnetized solar atmosphere. As a result, an array of scaling and multi-scaling
image-processing techniques can be used to measure the expected
self-organization of solar magnetic fields. While these techniques advance our
understanding of the physical system at work, it is unclear whether they can be
used to predict solar eruptions, thus obtaining a practical significance for
space weather. We address part of this problem by focusing on solar active
regions and by investigating the usefulness of scaling and multi-scaling
image-processing techniques in solar flare prediction. Since solar flares
exhibit spatial and temporal intermittency, we suggest that they are the
products of instabilities subject to a critical threshold in a turbulent
magnetic configuration. The identification of this threshold in scaling and
multi-scaling spectra would then contribute meaningfully to the prediction of
solar flares. We find that the fractal dimension of solar magnetic fields and
their multi-fractal spectrum of generalized correlation dimensions do not have
significant predictive ability. The respective multi-fractal structure
functions and their inertial-range scaling exponents, however, probably provide
some statistical distinguishing features between flaring and non-flaring active
regions. More importantly, the temporal evolution of the above scaling
exponents in flaring active regions probably shows a distinct behavior starting
a few hours prior to a flare and therefore this temporal behavior may be
practically useful in flare prediction. The results of this study need to be
validated by more comprehensive works over a large number of solar active
regions.Comment: 26 pages, 7 figure
Recognizing Degraded Handwritten Characters
In this paper, Slavonic manuscripts from the 11th
century written in Glagolitic script are
investigated. State-of-the-art optical character recognition methods produce poor results
for degraded handwritten document images. This is largely due to a lack of suitable
results from basic pre-processing steps such as binarization and image segmentation.
Therefore, a new, binarization-free approach will be presented that is independent of
pre-processing deficiencies. It additionally incorporates local information in order to
recognize also fragmented or faded characters. The proposed algorithm consists of
two steps: character classification and character localization. Firstly scale invariant
feature transform features are extracted and classified using support vector machines.
On this basis interest points are clustered according to their spatial information. Then,
characters are localized and eventually recognized by a weighted voting scheme of
pre-classified local descriptors. Preliminary results show that the proposed system can
handle highly degraded manuscript images with background noise, e.g. stains, tears,
and faded characters
Recovering Homography from Camera Captured Documents using Convolutional Neural Networks
Removing perspective distortion from hand held camera captured document
images is one of the primitive tasks in document analysis, but unfortunately,
no such method exists that can reliably remove the perspective distortion from
document images automatically. In this paper, we propose a convolutional neural
network based method for recovering homography from hand-held camera captured
documents.
Our proposed method works independent of document's underlying content and is
trained end-to-end in a fully automatic way. Specifically, this paper makes
following three contributions: Firstly, we introduce a large scale synthetic
dataset for recovering homography from documents images captured under
different geometric and photometric transformations; secondly, we show that a
generic convolutional neural network based architecture can be successfully
used for regressing the corners positions of documents captured under wild
settings; thirdly, we show that L1 loss can be reliably used for corners
regression. Our proposed method gives state-of-the-art performance on the
tested datasets, and has potential to become an integral part of document
analysis pipeline.Comment: 10 pages, 8 figure
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