2,712 research outputs found
Tensor-Based Methods for Blind Spatial Signature Estimation in Multidimensional Sensor Arrays
The estimation of spatial signatures and spatial frequencies is crucial for several practical applications such as radar, sonar, and wireless communications. In this paper, we propose two generalized iterative estimation algorithms to the case in which a multidimensional (R-D) sensor array is used at the receiver. The first tensor-based algorithm is an R-D blind spatial signature estimator that operates in scenarios where the source’s covariance matrix is nondiagonal and unknown. The second tensor-based algorithm is formulated for the case in which the sources are uncorrelated and exploits the dual-symmetry of the covariance tensor. Additionally, a new tensor-based formulation is proposed for an L-shaped array configuration. Simulation results show that our proposed schemes outperform the state-of-the-art matrix-based and tensor-based techniques
Dynamical spectral unmixing of multitemporal hyperspectral images
In this paper, we consider the problem of unmixing a time series of
hyperspectral images. We propose a dynamical model based on linear mixing
processes at each time instant. The spectral signatures and fractional
abundances of the pure materials in the scene are seen as latent variables, and
assumed to follow a general dynamical structure. Based on a simplified version
of this model, we derive an efficient spectral unmixing algorithm to estimate
the latent variables by performing alternating minimizations. The performance
of the proposed approach is demonstrated on synthetic and real multitemporal
hyperspectral images.Comment: 13 pages, 10 figure
Convective regularization for optical flow
We argue that the time derivative in a fixed coordinate frame may not be the
most appropriate measure of time regularity of an optical flow field. Instead,
for a given velocity field we consider the convective acceleration which describes the acceleration of objects moving according to
. Consequently we investigate the suitability of the nonconvex functional
as a regularization term for optical flow. We
demonstrate that this term acts as both a spatial and a temporal regularizer
and has an intrinsic edge-preserving property. We incorporate it into a
contrast invariant and time-regularized variant of the Horn-Schunck functional,
prove existence of minimizers and verify experimentally that it addresses some
of the problems of basic quadratic models. For the minimization we use an
iterative scheme that approximates the original nonlinear problem with a
sequence of linear ones. We believe that the convective acceleration may be
gainfully introduced in a variety of optical flow models
Mixture-Net: Low-Rank Deep Image Prior Inspired by Mixture Models for Spectral Image Recovery
This paper proposes a non-data-driven deep neural network for spectral image
recovery problems such as denoising, single hyperspectral image
super-resolution, and compressive spectral imaging reconstruction. Unlike
previous methods, the proposed approach, dubbed Mixture-Net, implicitly learns
the prior information through the network. Mixture-Net consists of a deep
generative model whose layers are inspired by the linear and non-linear
low-rank mixture models, where the recovered image is composed of a weighted
sum between the linear and non-linear decomposition. Mixture-Net also provides
a low-rank decomposition interpreted as the spectral image abundances and
endmembers, helpful in achieving remote sensing tasks without running
additional routines. The experiments show the MixtureNet effectiveness
outperforming state-of-the-art methods in recovery quality with the advantage
of architecture interpretability
Independent component analysis for non-standard data structures
Independent component analysis is a classical multivariate tool used for estimating independent sources among collections of mixed signals. However, modern forms of data are typically too complex for the basic theory to adequately handle. In this thesis extensions of independent component analysis to three cases of non-standard data structures are developed: noisy multivariate data, tensor-valued data and multivariate functional data.
In each case we define the corresponding independent component model along with the related assumptions and implications. The proposed estimators are mostly based on the use of kurtosis and its analogues for the considered structures, resulting into functionals of rather unified form, regardless of the type of the data. We prove the Fisher consistencies of the estimators and particular weight is given to their limiting distributions, using which comparisons between the methods are also made.Riippumattomien komponenttien analyysi on moniulotteisen tilastotieteen työkalu,jota käytetään estimoimaan riippumattomia lähdesignaaleja sekoitettujen signaalien joukosta. Modernit havaintoaineistot ovat kuitenkin tyypillisesti rakenteeltaan liian monimutkaisia, jotta niitä voitaisiin lähestyä alan perinteisillä menetelmillä. Tässä väitöskirjatyössä esitellään laajennukset riippumattomien komponenttien analyysin teoriasta kolmelle epästandardille aineiston muodolle: kohinaiselle moniulotteiselle datalle, tensoriarvoiselle datalle ja moniulotteiselle funktionaaliselle datalle.
Kaikissa tapauksissa määriteläään vastaava riippumattomien komponenttien malli oletuksineen ja seurauksineen. Esitellyt estimaattorit pohjautuvat enimmäkseen huipukkuuden ja sen laajennuksien käyttöönottoon ja saatavat funktionaalit ovat analyyttisesti varsin yhtenäisen muotoisia riippumatta aineiston tyypistä. Kaikille estimaattoreille näytetään niiden Fisher-konsistenttisuus ja painotettuna on erityisesti estimaattoreiden rajajakaumat, jotka mahdollistavat teoreettiset vertailut eri menetelmien välillä
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