The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Thomas A Courtade - One of the best experts on this subject based on the ideXlab platform.
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linear regression with shuffled data statistical and computational limits of permutation recovery
IEEE Transactions on Information Theory, 2018Co-Authors: Ashwin Pananjady, Martin J Wainwright, Thomas A CourtadeAbstract:Consider a noisy linear Observation Model with an unknown permutation, based on observing $y = \Pi ^{*} A x^{*} + w$ , where $x^{*} \in {\mathbb {R}} ^{d}$ is an unknown vector, $\Pi ^{*}$ is an unknown $n \times n$ permutation matrix, and $w \in {\mathbb {R}} ^{n}$ is additive Gaussian noise. We analyze the problem of permutation recovery in a random design setting in which the entries of matrix $A$ are drawn independently from a standard Gaussian distribution and establish sharp conditions on the signal-to-noise ratio, sample size $n$ , and dimension $d$ under which $\Pi ^{*}$ is exactly and approximately recoverable. On the computational front, we show that the maximum likelihood estimate of $\Pi ^{*}$ is NP-hard to compute for general $d$ , while also providing a polynomial time algorithm when $d =1$ .
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linear regression with an unknown permutation statistical and computational limits
Allerton Conference on Communication Control and Computing, 2016Co-Authors: Ashwin Pananjady, Martin J Wainwright, Thomas A CourtadeAbstract:Consider a noisy linear Observation Model with an unknown permutation, based on observing y = Π*Ax* + w, where x* ∈ ℝd is an unknown vector, Π* is an unknown n × n permutation matrix, and w ∈ ℝn is additive Gaussian noise. We analyze the problem of permutation recovery in a random design setting in which the entries of the matrix A are drawn i.i.d. from a standard Gaussian distribution, and establish sharp conditions on the SNR, sample size n, and dimension d under which Π* is exactly and approximately recoverable. On the computational front, we show that the maximum likelihood estimate of Π* is NP-hard to compute, while also providing a polynomial time algorithm when d = 1.
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linear regression with an unknown permutation statistical and computational limits
arXiv: Statistics Theory, 2016Co-Authors: Ashwin Pananjady, Martin J Wainwright, Thomas A CourtadeAbstract:Consider a noisy linear Observation Model with an unknown permutation, based on observing $y = \Pi^* A x^* + w$, where $x^* \in \mathbb{R}^d$ is an unknown vector, $\Pi^*$ is an unknown $n \times n$ permutation matrix, and $w \in \mathbb{R}^n$ is additive Gaussian noise. We analyze the problem of permutation recovery in a random design setting in which the entries of the matrix $A$ are drawn i.i.d. from a standard Gaussian distribution, and establish sharp conditions on the SNR, sample size $n$, and dimension $d$ under which $\Pi^*$ is exactly and approximately recoverable. On the computational front, we show that the maximum likelihood estimate of $\Pi^*$ is NP-hard to compute, while also providing a polynomial time algorithm when $d =1$.
Natalia Restrepocoupe - One of the best experts on this subject based on the ideXlab platform.
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understanding water and energy fluxes in the amazonia lessons from an Observation Model intercomparison
Global Change Biology, 2021Co-Authors: Natalia Restrepocoupe, Loren P Albert, Marcos Longo, Ian Baker, Naomi M Levine, Lina M MercadoAbstract:Tropical forests are an important part of global water and energy cycles, but the mechanisms that drive seasonality of their land-atmosphere exchanges have proven challenging to capture in Models. Here, we (1) report the seasonality of fluxes of latent heat (LE), sensible heat (H), and outgoing short and longwave radiation at four diverse tropical forest sites across Amazonia-along the equator from the Caxiuana and Tapajos National Forests in the eastern Amazon to a forest near Manaus, and from the equatorial zone to the southern forest in Reserva Jaru; (2) investigate how vegetation and climate influence these fluxes; and (3) evaluate land surface Model performance by comparing simulations to Observations. We found that previously identified failure of Models to capture observed dry-season increases in evapotranspiration (ET) was associated with Model overestimations of (1) magnitude and seasonality of Bowen ratios (relative to aseasonal Observations in which sensible was only 20%-30% of the latent heat flux) indicating Model exaggerated water limitation, (2) canopy emissivity and reflectance (albedo was only 10%-15% of incoming solar radiation, compared to 0.15%-0.22% simulated), and (3) vegetation temperatures (due to underestimation of dry-season ET and associated cooling). These partially compensating Model-Observation discrepancies (e.g., higher temperatures expected from excess Bowen ratios were partially ameliorated by brighter leaves and more interception/evaporation) significantly biased seasonal Model estimates of net radiation (Rn ), the key driver of water and energy fluxes (LE ~ 0.6 Rn and H ~ 0.15 Rn ), though these biases varied among sites and Models. A better representation of energy-related parameters associated with dynamic phenology (e.g., leaf optical properties, canopy interception, and skin temperature) could improve simulations and benchmarking of current vegetation-atmosphere exchange and reduce uncertainty of regional and global biogeochemical Models.
Panos Stinis - One of the best experts on this subject based on the ideXlab platform.
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improved particle filters for multi target tracking
Journal of Computational Physics, 2012Co-Authors: Vasileios Maroulas, Panos StinisAbstract:We present a novel approach for improving particle filters for multi-target tracking. The suggested approach is based on drift homotopy for stochastic differential equations. Drift homotopy is used to design a Markov Chain Monte Carlo step which is appended to the particle filter and aims to bring the particle filter samples closer to the Observations while at the same time respecting the target dynamics. We have used the proposed approach on the problem of multi-target tracking with a nonlinear Observation Model. The numerical results show that the suggested approach can improve significantly the performance of a particle filter.
Russell C Hardie - One of the best experts on this subject based on the ideXlab platform.
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map estimation for hyperspectral image resolution enhancement using an auxiliary sensor
IEEE Transactions on Image Processing, 2004Co-Authors: Russell C Hardie, Michael T Eismann, G L WilsonAbstract:This paper presents a novel maximum a posteriori estimator for enhancing the spatial resolution of an image using co-registered high spatial-resolution imagery from an auxiliary sensor. Here, we focus on the use of high-resolution panchromatic data to enhance hyperspectral imagery. However, the estimation framework developed allows for any number of spectral bands in the primary and auxiliary image. The proposed technique is suitable for applications where some correlation, either localized or global, exists between the auxiliary image and the image being enhanced. To exploit localized correlations, a spatially varying statistical Model, based on vector quantization, is used. Another important aspect of the proposed algorithm is that it allows for the use of an accurate Observation Model relating the "true" scene with the low-resolutions Observations. Experimental results with hyperspectral data derived from the airborne visible-infrared imaging spectrometer are presented to demonstrate the efficacy of the proposed estimator.
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high resolution image reconstruction from a sequence of rotated and translated frames and its application to an infrared imaging system
Optical Engineering, 1998Co-Authors: Russell C Hardie, Kenneth J Barnard, John G Bognar, Ernest E Armstrong, Edward A WatsonAbstract:Some imaging systems employ detector arrays which are not su‐ciently dense so as to meet the Nyquist criteria during image acquisition. This is particularly true for many staring infrared imagers. Thus, the full resolution afiorded by the optics is not being realized in such a system. This paper presents a technique for estimating a high resolution image, with reduced aliasing, from a sequence of undersampled rotated and translationally shifted frames. Such an image sequence can be obtained if an imager is mounted on a moving platform, such as an aircraft. Several approaches to this type of problem have been proposed in the literature. Here we extend some of this previous work. In particular, we deflne an Observation Model which incorporates knowledge of the optical system and detector array. The high resolution image estimate is formed by minimizing a new regularized cost function which is based on the Observation Model. We show that with the proper choice of a tuning parameter, our algorithm exhibits robustness in the presence of noise. We consider both gradient descent and conjugate gradient optimization procedures to minimize the cost function. Detailed experimental results are provided to illustrate the performance of the proposed algorithm using digital video from an infrared imager.
Jeanfrancois Giovannelli - One of the best experts on this subject based on the ideXlab platform.
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an improved Observation Model for super resolution under affine motion
arXiv: Applications, 2009Co-Authors: G Rochefort, Frederic Champagnat, Le G Besnerais, Jeanfrancois GiovannelliAbstract:Super-resolution (SR) techniques make use of subpixel shifts between frames in an image sequence to yield higher-resolution images. We propose an original Observation Model devoted to the case of non isometric inter-frame motion as required, for instance, in the context of airborne imaging sensors. First, we describe how the main Observation Models used in the SR literature deal with motion, and we explain why they are not suited for non isometric motion. Then, we propose an extension of the Observation Model by Elad and Feuer adapted to affine motion. This Model is based on a decomposition of affine transforms into successive shear transforms, each one efficiently implemented by row-by-row or column-by-column 1-D affine transforms. We demonstrate on synthetic and real sequences that our Observation Model incorporated in a SR reconstruction technique leads to better results in the case of variable scale motions and it provides equivalent results in the case of isometric motions.
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an improved Observation Model for super resolution under affine motion
IEEE Transactions on Image Processing, 2006Co-Authors: G Rochefort, Frederic Champagnat, Le G Besnerais, Jeanfrancois GiovannelliAbstract:Super-resolution (SR) techniques make use of subpixel shifts between frames in an image sequence to yield higher resolution images. We propose an original Observation Model devoted to the case of nonisometric inter-frame motion as required, for instance, in the context of airborne imaging sensors. First, we describe how the main Observation Models used in the SR literature deal with motion, and we explain why they are not suited for nonisometric motion. Then, we propose an extension of the Observation Model by Elad and Feuer adapted to affine motion. This Model is based on a decomposition of affine transforms into successive shear transforms, each one efficiently implemented by row-by-row or column-by-column one-dimensional affine transforms. We demonstrate on synthetic and real sequences that our Observation Model incorporated in a SR reconstruction technique leads to better results in the case of variable scale motions and it provides equivalent results in the case of isometric motions
