15,097 research outputs found
S4Net: Single Stage Salient-Instance Segmentation
We consider an interesting problem-salient instance segmentation in this
paper. Other than producing bounding boxes, our network also outputs
high-quality instance-level segments. Taking into account the
category-independent property of each target, we design a single stage salient
instance segmentation framework, with a novel segmentation branch. Our new
branch regards not only local context inside each detection window but also its
surrounding context, enabling us to distinguish the instances in the same scope
even with obstruction. Our network is end-to-end trainable and runs at a fast
speed (40 fps when processing an image with resolution 320x320). We evaluate
our approach on a publicly available benchmark and show that it outperforms
other alternative solutions. We also provide a thorough analysis of the design
choices to help readers better understand the functions of each part of our
network. The source code can be found at
\url{https://github.com/RuochenFan/S4Net}
On the detection of spectral ripples from the Recombination Epoch
Photons emitted during the epochs of Hydrogen () and Helium recombination ( for HeII
HeI, for HeIII
HeII) are predicted to appear as broad, weak spectral distortions of the Cosmic
Microwave Background. We present a feasibility study for a ground-based
experimental detection of these recombination lines, which would provide an
observational constraint on the thermal ionization history of the Universe,
uniquely probing astrophysical cosmology beyond the last scattering surface. We
find that an octave band in the 2--6 GHz window is optimal for such an
experiment, both maximizing signal-to-noise ratio and including sufficient line
spectral structure. At these frequencies the predicted signal appears as an
additive quasi-sinusoidal component with amplitude about nK that is
embedded in a sky spectrum some nine orders of magnitude brighter. We discuss
an algorithm to detect these tiny spectral fluctuations in the sky spectrum by
foreground modeling. We introduce a \textit{Maximally Smooth} function capable
of describing the foreground spectrum and distinguishing the signal of
interest. With Bayesian statistical tests and mock data we estimate that a
detection of the predicted distortions is possible with 90\% confidence by
observing for 255 days with an array of 128 radiometers using cryogenically
cooled state-of-the-art receivers. We conclude that detection is in principle
feasible in realistic observing times; we propose APSERa---Array of Precision
Spectrometers for the Epoch of Recombination---a dedicated radio telescope to
detect these recombination lines.Comment: 33 pages, 16 figures, submitted to ApJ, comments welcom
A Fusion Framework for Camouflaged Moving Foreground Detection in the Wavelet Domain
Detecting camouflaged moving foreground objects has been known to be
difficult due to the similarity between the foreground objects and the
background. Conventional methods cannot distinguish the foreground from
background due to the small differences between them and thus suffer from
under-detection of the camouflaged foreground objects. In this paper, we
present a fusion framework to address this problem in the wavelet domain. We
first show that the small differences in the image domain can be highlighted in
certain wavelet bands. Then the likelihood of each wavelet coefficient being
foreground is estimated by formulating foreground and background models for
each wavelet band. The proposed framework effectively aggregates the
likelihoods from different wavelet bands based on the characteristics of the
wavelet transform. Experimental results demonstrated that the proposed method
significantly outperformed existing methods in detecting camouflaged foreground
objects. Specifically, the average F-measure for the proposed algorithm was
0.87, compared to 0.71 to 0.8 for the other state-of-the-art methods.Comment: 13 pages, accepted by IEEE TI
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