37,410 research outputs found
A Reverse Hierarchy Model for Predicting Eye Fixations
A number of psychological and physiological evidences suggest that early
visual attention works in a coarse-to-fine way, which lays a basis for the
reverse hierarchy theory (RHT). This theory states that attention propagates
from the top level of the visual hierarchy that processes gist and abstract
information of input, to the bottom level that processes local details.
Inspired by the theory, we develop a computational model for saliency detection
in images. First, the original image is downsampled to different scales to
constitute a pyramid. Then, saliency on each layer is obtained by image
super-resolution reconstruction from the layer above, which is defined as
unpredictability from this coarse-to-fine reconstruction. Finally, saliency on
each layer of the pyramid is fused into stochastic fixations through a
probabilistic model, where attention initiates from the top layer and
propagates downward through the pyramid. Extensive experiments on two standard
eye-tracking datasets show that the proposed method can achieve competitive
results with state-of-the-art models.Comment: CVPR 2014, 27th IEEE Conference on Computer Vision and Pattern
Recognition (CVPR). CVPR 201
EEG Classification based on Image Configuration in Social Anxiety Disorder
The problem of detecting the presence of Social Anxiety Disorder (SAD) using
Electroencephalography (EEG) for classification has seen limited study and is
addressed with a new approach that seeks to exploit the knowledge of EEG sensor
spatial configuration. Two classification models, one which ignores the
configuration (model 1) and one that exploits it with different interpolation
methods (model 2), are studied. Performance of these two models is examined for
analyzing 34 EEG data channels each consisting of five frequency bands and
further decomposed with a filter bank. The data are collected from 64 subjects
consisting of healthy controls and patients with SAD. Validity of our
hypothesis that model 2 will significantly outperform model 1 is borne out in
the results, with accuracy -- higher for model 2 for each machine
learning algorithm we investigated. Convolutional Neural Networks (CNN) were
found to provide much better performance than SVM and kNNs
Automatic Response Assessment in Regions of Language Cortex in Epilepsy Patients Using ECoG-based Functional Mapping and Machine Learning
Accurate localization of brain regions responsible for language and cognitive
functions in Epilepsy patients should be carefully determined prior to surgery.
Electrocorticography (ECoG)-based Real Time Functional Mapping (RTFM) has been
shown to be a safer alternative to the electrical cortical stimulation mapping
(ESM), which is currently the clinical/gold standard. Conventional methods for
analyzing RTFM signals are based on statistical comparison of signal power at
certain frequency bands. Compared to gold standard (ESM), they have limited
accuracies when assessing channel responses.
In this study, we address the accuracy limitation of the current RTFM signal
estimation methods by analyzing the full frequency spectrum of the signal and
replacing signal power estimation methods with machine learning algorithms,
specifically random forest (RF), as a proof of concept. We train RF with power
spectral density of the time-series RTFM signal in supervised learning
framework where ground truth labels are obtained from the ESM. Results obtained
from RTFM of six adult patients in a strictly controlled experimental setup
reveal the state of the art detection accuracy of for the
language comprehension task, an improvement of over the conventional
RTFM estimation method. To the best of our knowledge, this is the first study
exploring the use of machine learning approaches for determining RTFM signal
characteristics, and using the whole-frequency band for better region
localization. Our results demonstrate the feasibility of machine learning based
RTFM signal analysis method over the full spectrum to be a clinical routine in
the near future.Comment: This paper will appear in the Proceedings of IEEE International
Conference on Systems, Man and Cybernetics (SMC) 201
Topology of structure in the Sloan Digital Sky Survey: model testing
We measure the three-dimensional topology of large-scale structure in the
Sloan Digital Sky Survey (SDSS). This allows the genus statistic to be measured
with unprecedented statistical accuracy. The sample size is now sufficiently
large to allow the topology to be an important tool for testing galaxy
formation models. For comparison, we make mock SDSS samples using several
state-of-the-art N-body simulations: the Millennium run of Springel et al.
(2005)(10 billion particles), Kim & Park (2006) CDM models (1.1 billion
particles), and Cen & Ostriker (2006) hydrodynamic code models (8.6 billion
cell hydro mesh). Each of these simulations uses a different method for
modeling galaxy formation. The SDSS data show a genus curve that is broadly
characteristic of that produced by Gaussian random phase initial conditions.
Thus the data strongly support the standard model of inflation where Gaussian
random phase initial conditions are produced by random quantum fluctuations in
the early universe. But on top of this general shape there are measurable
differences produced by non-linear gravitational effects (cf. Matsubara 1994),
and biasing connected with galaxy formation. The N-body simulations have been
tuned to reproduce the power spectrum and multiplicity function but not
topology, so topology is an acid test for these models. The data show a
``meatball'' shift (only partly due to the Sloan Great Wall of Galaxies; this
shift also appears in a sub-sample not containing the Wall) which differs at
the 2.5\sigma level from the results of the Millennium run and the Kim & Park
dark halo models, even including the effects of cosmic variance.Comment: 13 Apj pages, 7 figures High-resolution stereo graphic available at
http://www.astro.princeton.edu/~dclayh/stereo50.ep
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