43,929 research outputs found
Salient Object Detection via Augmented Hypotheses
In this paper, we propose using \textit{augmented hypotheses} which consider
objectness, foreground and compactness for salient object detection. Our
algorithm consists of four basic steps. First, our method generates the
objectness map via objectness hypotheses. Based on the objectness map, we
estimate the foreground margin and compute the corresponding foreground map
which prefers the foreground objects. From the objectness map and the
foreground map, the compactness map is formed to favor the compact objects. We
then derive a saliency measure that produces a pixel-accurate saliency map
which uniformly covers the objects of interest and consistently separates fore-
and background. We finally evaluate the proposed framework on two challenging
datasets, MSRA-1000 and iCoSeg. Our extensive experimental results show that
our method outperforms state-of-the-art approaches.Comment: IJCAI 2015 pape
Training Group Orthogonal Neural Networks with Privileged Information
Learning rich and diverse representations is critical for the performance of
deep convolutional neural networks (CNNs). In this paper, we consider how to
use privileged information to promote inherent diversity of a single CNN model
such that the model can learn better representations and offer stronger
generalization ability. To this end, we propose a novel group orthogonal
convolutional neural network (GoCNN) that learns untangled representations
within each layer by exploiting provided privileged information and enhances
representation diversity effectively. We take image classification as an
example where image segmentation annotations are used as privileged information
during the training process. Experiments on two benchmark datasets -- ImageNet
and PASCAL VOC -- clearly demonstrate the strong generalization ability of our
proposed GoCNN model. On the ImageNet dataset, GoCNN improves the performance
of state-of-the-art ResNet-152 model by absolute value of 1.2% while only uses
privileged information of 10% of the training images, confirming effectiveness
of GoCNN on utilizing available privileged knowledge to train better CNNs.Comment: Proceedings of the IJCAI-1
First Year Wilkinson Microwave Anisotropy Probe (WMAP) Observations: Angular Power Spectrum
We present the angular power spectrum derived from the first-year Wilkinson
Microwave Anisotropy Probe (WMAP) sky maps. We study a variety of power
spectrum estimation methods and data combinations and demonstrate that the
results are robust. The data are modestly contaminated by diffuse Galactic
foreground emission, but we show that a simple Galactic template model is
sufficient to remove the signal. Point sources produce a modest contamination
in the low frequency data. After masking ~700 known bright sources from the
maps, we estimate residual sources contribute ~3500 uK^2 at 41 GHz, and ~130
uK^2 at 94 GHz, to the power spectrum l*(l+1)*C_l/(2*pi) at l=1000. Systematic
errors are negligible compared to the (modest) level of foreground emission.
Our best estimate of the power spectrum is derived from 28 cross-power spectra
of statistically independent channels. The final spectrum is essentially
independent of the noise properties of an individual radiometer. The resulting
spectrum provides a definitive measurement of the CMB power spectrum, with
uncertainties limited by cosmic variance, up to l~350. The spectrum clearly
exhibits a first acoustic peak at l=220 and a second acoustic peak at l~540 and
it provides strong support for adiabatic initial conditions. Kogut et al.
(2003) analyze the C_l^TE power spectrum, and present evidence for a relatively
high optical depth, and an early period of cosmic reionization. Among other
things, this implies that the temperature power spectrum has been suppressed by
\~30% on degree angular scales, due to secondary scattering.Comment: One of thirteen companion papers on first-year WMAP results submitted
to ApJ; 44 pages, 14 figures; a version with higher quality figures is also
available at http://lambda.gsfc.nasa.gov/product/map/map_bibliography.htm
Maximum likelihood, parametric component separation and CMB B-mode detection in suborbital experiments
We investigate the performance of the parametric Maximum Likelihood component
separation method in the context of the CMB B-mode signal detection and its
characterization by small-scale CMB suborbital experiments. We consider
high-resolution (FWHM=8') balloon-borne and ground-based observatories mapping
low dust-contrast sky areas of 400 and 1000 square degrees, in three frequency
channels, 150, 250, 410 GHz, and 90, 150, 220 GHz, with sensitivity of order 1
to 10 micro-K per beam-size pixel. These are chosen to be representative of
some of the proposed, next-generation, bolometric experiments. We study the
residual foreground contributions left in the recovered CMB maps in the pixel
and harmonic domain and discuss their impact on a determination of the
tensor-to-scalar ratio, r. In particular, we find that the residuals derived
from the simulated data of the considered balloon-borne observatories are
sufficiently low not to be relevant for the B-mode science. However, the
ground-based observatories are in need of some external information to permit
satisfactory cleaning. We find that if such information is indeed available in
the latter case, both the ground-based and balloon-borne experiments can detect
the values of r as low as ~0.04 at 95% confidence level. The contribution of
the foreground residuals to these limits is found to be then subdominant and
these are driven by the statistical uncertainty due to CMB, including E-to-B
leakage, and noise. We emphasize that reaching such levels will require a
sufficient control of the level of systematic effects present in the data.Comment: 18 pages, 12 figures, 6 table
Detection thresholding using mutual information
In this paper, we introduce a novel non-parametric thresholding method that we term Mutual-Information
Thresholding. In our approach, we choose the two detection thresholds for two input signals such that the
mutual information between the thresholded signals is maximised. Two efficient algorithms implementing our
idea are presented: one using dynamic programming to fully explore the quantised search space and the other
method using the Simplex algorithm to perform gradient ascent to significantly speed up the search, under the
assumption of surface convexity. We demonstrate the effectiveness of our approach in foreground detection
(using multi-modal data) and as a component in a person detection system
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