37,907 research outputs found
Deterministic Constructions of Binary Measurement Matrices from Finite Geometry
Deterministic constructions of measurement matrices in compressed sensing
(CS) are considered in this paper. The constructions are inspired by the recent
discovery of Dimakis, Smarandache and Vontobel which says that parity-check
matrices of good low-density parity-check (LDPC) codes can be used as
{provably} good measurement matrices for compressed sensing under
-minimization. The performance of the proposed binary measurement
matrices is mainly theoretically analyzed with the help of the analyzing
methods and results from (finite geometry) LDPC codes. Particularly, several
lower bounds of the spark (i.e., the smallest number of columns that are
linearly dependent, which totally characterizes the recovery performance of
-minimization) of general binary matrices and finite geometry matrices
are obtained and they improve the previously known results in most cases.
Simulation results show that the proposed matrices perform comparably to,
sometimes even better than, the corresponding Gaussian random matrices.
Moreover, the proposed matrices are sparse, binary, and most of them have
cyclic or quasi-cyclic structure, which will make the hardware realization
convenient and easy.Comment: 12 pages, 11 figure
Superpixel-based Two-view Deterministic Fitting for Multiple-structure Data
This paper proposes a two-view deterministic geometric model fitting method,
termed Superpixel-based Deterministic Fitting (SDF), for multiple-structure
data. SDF starts from superpixel segmentation, which effectively captures prior
information of feature appearances. The feature appearances are beneficial to
reduce the computational complexity for deterministic fitting methods. SDF also
includes two original elements, i.e., a deterministic sampling algorithm and a
novel model selection algorithm. The two algorithms are tightly coupled to
boost the performance of SDF in both speed and accuracy. Specifically, the
proposed sampling algorithm leverages the grouping cues of superpixels to
generate reliable and consistent hypotheses. The proposed model selection
algorithm further makes use of desirable properties of the generated
hypotheses, to improve the conventional fit-and-remove framework for more
efficient and effective performance. The key characteristic of SDF is that it
can efficiently and deterministically estimate the parameters of model
instances in multi-structure data. Experimental results demonstrate that the
proposed SDF shows superiority over several state-of-the-art fitting methods
for real images with single-structure and multiple-structure data.Comment: Accepted by European Conference on Computer Vision (ECCV
Estimation of Overspread Scattering Functions
In many radar scenarios, the radar target or the medium is assumed to possess
randomly varying parts. The properties of a target are described by a random
process known as the spreading function. Its second order statistics under the
WSSUS assumption are given by the scattering function. Recent developments in
operator sampling theory suggest novel channel sounding procedures that allow
for the determination of the spreading function given complete statistical
knowledge of the operator echo from a single sounding by a weighted pulse
train.
We construct and analyze a novel estimator for the scattering function based
on these findings. Our results apply whenever the scattering function is
supported on a compact subset of the time-frequency plane. We do not make any
restrictions either on the geometry of this support set, or on its area. Our
estimator can be seen as a generalization of an averaged periodogram estimator
for the case of a non-rectangular geometry of the support set of the scattering
function
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