154 research outputs found
Completing Low-Rank Matrices with Corrupted Samples from Few Coefficients in General Basis
Subspace recovery from corrupted and missing data is crucial for various
applications in signal processing and information theory. To complete missing
values and detect column corruptions, existing robust Matrix Completion (MC)
methods mostly concentrate on recovering a low-rank matrix from few corrupted
coefficients w.r.t. standard basis, which, however, does not apply to more
general basis, e.g., Fourier basis. In this paper, we prove that the range
space of an matrix with rank can be exactly recovered from few
coefficients w.r.t. general basis, though and the number of corrupted
samples are both as high as . Our model covers
previous ones as special cases, and robust MC can recover the intrinsic matrix
with a higher rank. Moreover, we suggest a universal choice of the
regularization parameter, which is . By our
filtering algorithm, which has theoretical guarantees, we can
further reduce the computational cost of our model. As an application, we also
find that the solutions to extended robust Low-Rank Representation and to our
extended robust MC are mutually expressible, so both our theory and algorithm
can be applied to the subspace clustering problem with missing values under
certain conditions. Experiments verify our theories.Comment: To appear in IEEE Transactions on Information Theor
Inner and Inter Label Propagation: Salient Object Detection in the Wild
In this paper, we propose a novel label propagation based method for saliency
detection. A key observation is that saliency in an image can be estimated by
propagating the labels extracted from the most certain background and object
regions. For most natural images, some boundary superpixels serve as the
background labels and the saliency of other superpixels are determined by
ranking their similarities to the boundary labels based on an inner propagation
scheme. For images of complex scenes, we further deploy a 3-cue-center-biased
objectness measure to pick out and propagate foreground labels. A
co-transduction algorithm is devised to fuse both boundary and objectness
labels based on an inter propagation scheme. The compactness criterion decides
whether the incorporation of objectness labels is necessary, thus greatly
enhancing computational efficiency. Results on five benchmark datasets with
pixel-wise accurate annotations show that the proposed method achieves superior
performance compared with the newest state-of-the-arts in terms of different
evaluation metrics.Comment: The full version of the TIP 2015 publicatio
Face Recognition from Sequential Sparse 3D Data via Deep Registration
Previous works have shown that face recognition with high accurate 3D data is
more reliable and insensitive to pose and illumination variations. Recently,
low-cost and portable 3D acquisition techniques like ToF(Time of Flight) and
DoE based structured light systems enable us to access 3D data easily, e.g.,
via a mobile phone. However, such devices only provide sparse(limited speckles
in structured light system) and noisy 3D data which can not support face
recognition directly. In this paper, we aim at achieving high-performance face
recognition for devices equipped with such modules which is very meaningful in
practice as such devices will be very popular. We propose a framework to
perform face recognition by fusing a sequence of low-quality 3D data. As 3D
data are sparse and noisy which can not be well handled by conventional methods
like the ICP algorithm, we design a PointNet-like Deep Registration
Network(DRNet) which works with ordered 3D point coordinates while preserving
the ability of mining local structures via convolution. Meanwhile we develop a
novel loss function to optimize our DRNet based on the quaternion expression
which obviously outperforms other widely used functions. For face recognition,
we design a deep convolutional network which takes the fused 3D depth-map as
input based on AMSoftmax model. Experiments show that our DRNet can achieve
rotation error 0.95{\deg} and translation error 0.28mm for registration. The
face recognition on fused data also achieves rank-1 accuracy 99.2% , FAR-0.001
97.5% on Bosphorus dataset which is comparable with state-of-the-art
high-quality data based recognition performance.Comment: To be appeared in ICB201
Coastal Upwelling Off the China Coasts
Upwelling is an important oceanographic phenomenon that brings cooler and nutrient-rich water upward to the surface, facilitating the growth of phytoplankton and other primary producers, which results in high levels of primary productivity and hence fishery production. This chapter presents a review of recent studies on six major upwelling regions along the China coasts, with a focus on the eastern and southeastern coasts of mainland China, based on in situ measurements, satellite observations and numerical simulations. These upwelling regions result primarily from the summer monsoon winds, though other mechanisms, such as river discharge, baroclinicity, topography, tides, and the presence of mean current, may also be in play. In this review, their impacts on local biogeochemical processes are briefly summarized. Also discussed are their possible responses to the globally changing climate
A comparative study of Grid and Natural sentences effects on Normal-to-Lombard conversion
Grid sentence is commonly used for studying the Lombard effect and
Normal-to-Lombard conversion. However, it's unclear if Normal-to-Lombard models
trained on grid sentences are sufficient for improving natural speech
intelligibility in real-world applications. This paper presents the recording
of a parallel Lombard corpus (called Lombard Chinese TIMIT, LCT) extracting
natural sentences from Chinese TIMIT. Then We compare natural and grid
sentences in terms of Lombard effect and Normal-to-Lombard conversion using LCT
and Enhanced MAndarin Lombard Grid corpus (EMALG). Through a parametric
analysis of the Lombard effect, We find that as the noise level increases, both
natural sentences and grid sentences exhibit similar changes in parameters, but
in terms of the increase of the alpha ratio, grid sentences show a greater
increase. Following a subjective intelligibility assessment across genders and
Signal-to-Noise Ratios, the StarGAN model trained on EMALG consistently
outperforms the model trained on LCT in terms of improving intelligibility.
This superior performance may be attributed to EMALG's larger alpha ratio
increase from normal to Lombard speech
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