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Deep Robust Regression 7

Deep Robust Regression

引用

7th ieee international workshop on computational advances in multi-sensor adaptive processing (CAMSAP)

作者： Diskin, Tzvi Draskovic, Gordana Pascal, Frederic Wiesel, Ami Hebrew Univ Jerusalem Sch Comp Sci & Engn Edmond J Safra Campus IL-9190416 Jerusalem Israel Univ Paris Sud CNRS Cent Supelec L2S 3 Rue Joliot Curie F-91190 Gif Sur Yvette France

ISBN: (纸本)9781538612514

In this paper, we consider the use of deep neural networks in the context of robust regression. We address the standard linear model with observations that are corrupted by outliers. We build upon Huber's robust regression and the classical least trimmed squares estimator, and propose a deep neural network that generalizes both and provides high accuracy with low computational complexity. The network is trained for arbitrary linear models using a single training phase. Numerical experiments with synthetic data demonstrate that the network can handle on a large range of Signal-to-Noise Ratio (SNR) and is robust to different types of outliers.

关键词： Robust Regression Deep Learning Neural Networks

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multi-scale Histogram Tone Mapping Algorithm for Display of Wide Dynamic Range Images 7

Multi-scale Histogram Tone Mapping Algorithm for Display of ...

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7th ieee international workshop on computational advances in multi-sensor adaptive processing (CAMSAP)

作者： Yang, Jie Hare, Alain Shahnovich, Ulian Yadid-Pecht, Orly Univ Calgary Schulich Sch Engn Integrated Intelligent Sensors Lab Lab I2Sense 2500 Univ Dr NW Calgary AB T2N 1N4 Canada

ISBN: (纸本)9781538612514

Maintaining brightness and preserving image contrast can be challenging when tone mapping wide dynamic range (WDR) images. In this paper, we present a novel tone mapping algorithm for compressing of WDR images. The algorithm processes WDR images in different scales. The overall brightness consistency and fine details are well preserved in large and small scales, respectively. A fusion method based on a multi-scale guided image filter is also proposed to synthesize all scales to generate the final image. Integral image and integral histogram are utilized in this algorithm which greatly reduces computation complexity and processing time. Experimental results show that our algorithm can produce visually appealing images with good brightness and high local contrast.

关键词： Microsoft Windows Brightness Histograms Dynamic range Heuristic algorithms Image coding Conferences

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Optical Flow Estimation in Ultrasound Images Using a Sparse Representation 7

Optical Flow Estimation in Ultrasound Images Using a Sparse ...

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7th ieee international workshop on computational advances in multi-sensor adaptive processing (CAMSAP)

作者： Ouzir, N. Tourneret, J. -Y. Basarab, A. Univ Toulouse INP ENSEEIHT IRIT TeSA 2 Rue Camichel F-31071 Toulouse 7 France Univ Toulouse IRIT CNRS UMR 5505 118 Route Narbonne F-31062 Toulouse 9 France

ISBN: (纸本)9781538612514

This paper introduces a 2D optical flow estimation method for cardiac ultrasound imaging based on a sparse representation. The optical flow problem is regularized using a classical gradient-based smoothness term combined with a sparsity inducing regularization that uses a learned cardiac flow dictionary. A particular emphasis is put on the influence of the spatial and sparse regularizations on the optical flow estimation problem. A comparison with state-of-the-art methods using realistic simulations shows the competitiveness of the proposed method for cardiac motion estimation in ultrasound images.

关键词： Optical flow sparse representations cardiac ultrasound motion estimation dictionary learning

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Block term decomposition with rank estimation using group sparsity 7

Block term decomposition with rank estimation using group sp...

引用

7th ieee international workshop on computational advances in multi-sensor adaptive processing (CAMSAP)

作者： Han, Xu Albera, Laurent Kachenoura, Amar Shu, Huazhong Senhadji, Lotfi INSERM U1099 F-35402 Rennes France Univ Rennes 1 LTSI Campus Beaulieu F-35042 Rennes France Southeast Univ LIST 2 Sipailou Nanjing 210096 Jiangsu Peoples R China Ctr Rech Informat Biomed Sino Francais CRIBs Rennes France

ISBN: (纸本)9781538612514

In this paper, we propose a new rank-(L, L, 1) Block Term Decomposition (BTD) method. Contrarily to classical techniques, the proposed method estimates also the number of terms and the rank-(L, L, 1) of each term from an overestimated initialization of them. This is achieved by using Group Sparsity of the Loading (GSL) matrices. Numerical experiments with noisy tensors show the good behavior of GSL-BTD and its robustness with respect to the presence of noise in comparison with classical methods. Experiments on epileptic signals confirm its efficiency in practical contexts.

关键词： Loading Tensile stress Estimation Signal to noise ratio Electroencephalography Noise measurement Conferences

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Statistical Modeling and Classification of Reflectance Confocal Microscopy Images 7

Statistical Modeling and Classification of Reflectance Confo...

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7th ieee international workshop on computational advances in multi-sensor adaptive processing (CAMSAP)

作者： Halimi, Abdelghafour Batatia, Hadj Le Digabel, Jimmy Josse, Gwendal Tourneret, Jean-Yves Univ Toulouse ENSEEIHT IRIT 2 Rue CamichelBP 7122 F-31071 Toulouse 7 France Ctr Rech Peau Pierre Fabre Dermocosmet 2 Rue Viguerie F-31025 Toulouse 3 France

ISBN: (纸本)9781538612514

This paper deals with the characterization and classification of reflectance confocal microscopy images of human skin. A special attention will be given to the identification and characterization of the lentigo, a phenomenon that originates at the dermo-epidermic junction of the skin. Confocal images are acquired at different skin depths with a high resolution. For each depth, the histograms of pixel intensities are determined, and well statistically modelled with a generalized gamma distribution (GGD). The scale, shape and translation parameters associated with the GGD are estimated using a new natural gradient descent algorithm showing fast convergence properties when compared to state-of-the-art estimation methods. Results show that the estimated parameters can be used to classify clinical images of lentigo and healthy patients. They also show that the scale and shape parameters are good features to identify and characterize the presence of lentigo in skin tissues.

关键词： Reflectance confocal microscopy lentigo characterization maximum likelihood estimation natural gradient

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A Fast Algorithm Based on a Sylvester-like Equation for LS Regression with GMRF Prior 7

A Fast Algorithm Based on a Sylvester-like Equation for LS R...

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7th ieee international workshop on computational advances in multi-sensor adaptive processing (CAMSAP)

作者： Wei, Qi Chouzenoux, Emilie Tourneret, Jean-Yves Pesquet, Jean-Christophe Duke Univ Dept ECE Durham NC 27710 USA Univ Paris Est Marne la Vallee LIGM F-77454 Champs Sur Marne France Univ Toulouse IRIT INP ENSEEIHT F-31071 Toulouse France Univ Paris Saclay Cent Supelec Ctr Visual Comp F-92295 Chatenay Malabry France

ISBN: (纸本)9781538612514

This paper presents a fast approach for penalized least squares (LS) regression problems using a 2D Gaussian Markov random field (GMRF) prior. More precisely, the computation of the proximity operator of the LS criterion regularized by different GMRF potentials is formulated as solving a Sylvester-like matrix equation. By exploiting the structural properties of GMRFs, this matrix equation is solved column-wise in an analytical way. The proposed algorithm can be embedded into a wide range of proximal algorithms to solve LS regression problems including a convex penalty. Experiments carried out in the case of a constrained LS regression problem arising in a multichannel image processing application, provide evidence that an alternating direction method of multipliers performs quite efficiently in this context.

关键词： Mathematical model Matrix decomposition Two dimensional displays Markov processes Conferences computational modeling Optimization

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Gaussian Sum Particle Flow Filter 7

Gaussian Sum Particle Flow Filter

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7th ieee international workshop on computational advances in multi-sensor adaptive processing (CAMSAP)

作者： Pal, Soumyasundar Coates, Mark McGill Univ Dept Elect & Comp Engn 3480 Univ St Montreal PQ H3A 2A7 Canada

ISBN: (纸本)9781538612514

Particle flow filters provide an approach for state estimation in nonlinear systems. They can outperform many particle filter implementations when the state dimension is high or when the measurements are highly informative. Instead of employing importance sampling, the particles are migrated by numerically solving differential equations that describe a flow from the prior to the posterior at each time step. An analytical solution for the flow equation requires a Gaussian assumption for both the prior and the posterior. Recently Khan et al. [1] devised an approximate flow that could address the case when the prior is represented by a Gaussian Mixture Model (GMM) and the likelihood function is Gaussian. The solution involved inversion of a large matrix which made the computational requirements scale poorly with the state dimension. In this paper, we devise an approximate particle flow filter for the case when both the prior and the likelihood are modeled using Gaussian mixtures. We perform numerical experiments to explore when the proposed method offers advantages compared to existing techniques.

关键词： non-linear sequential state estimation particle flow Daum-Huang filter particle filter Gaussian mixture model high-dimensional filtering

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Simulated Convergence Rates with Application to an Intractable α-Stable Inference Problem 7

Simulated Convergence Rates with Application to an Intractab...

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7th ieee international workshop on computational advances in multi-sensor adaptive processing (CAMSAP)

作者： Riabiz, Marina Ardeshiri, Tohid Kontoyiannis, Ioannis Godsill, Simon Univ Cambridge Dept Engn Cambridge CB2 1PZ England Linkoping Univ Dept Elect Engn SE-58183 Linkoping Sweden Athens Univ Econ & Business Dept Informat Patiss 76 Athens 10434 Greece

ISBN: (纸本)9781538612514

We report the results of a series of numerical studies examining the convergence rate for some approximate representations of alpha-stable distributions, which are a highly intractable class of distributions for inference purposes. Our proposed representation turns the intractable inference for an infinite-dimensional series of parameters into an (approximately) conditionally Gaussian representation, to which standard inference procedures such as Expectation-Maximization (EM), Markov chain Monte Carlo (MCMC) and Particle Filtering can be readily applied. While we have previously proved the asymptotic convergence of this representation, here we study the rate of this convergence for finite values of a truncation parameter, c. This allows the selection of appropriate truncations for different parameter configurations and for the accuracy required for the model. The convergence is examined directly in terms of cumulative distribution functions and densities, through the application of the Berry theorems and Parseval theorems. Our results indicate that the behaviour of our representations is significantly superior to that of representations that simply truncate the series with no Gaussian residual term.

关键词： Probability density function Convergence Standards Random variables Conferences Distribution functions Numerical simulation

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Cramer-Rao-Induced Bound for Interference-to-Signal Ratio Achievable through Non-Gaussian Independent Component Extraction 7

Cramer-Rao-Induced Bound for Interference-to-Signal Ratio Ac...

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7th ieee international workshop on computational advances in multi-sensor adaptive processing (CAMSAP)

作者： Kautsky, Vaclav Koldovsky, Zbynek Tichavsky, Petr Czech Tech Univ Fac Nucl Sci & Phys Engn Prague Czech Republic Tech Univ Liberec Fac Mech Informat & Interdisciplinary Studies Studentska 2 Liberec 46117 Czech Republic Inst Informat Theory & Automat Pod Vodarenskou Vezi 4POB 18 Prague 18208 8 Czech Republic

ISBN: (纸本)9781538612514

This paper deals with the Cramer-Rao Lower Bound (CRLB) for a novel blind source separation method called Independent Component Extraction (ICE). Compared to Independent Component Analysis (ICA), ICE aims to extract only one independent signal from a linear mixture. The target signal is assumed to be non-Gaussian, while the other signals, which are not separated, are modeled as a Gaussian mixture. A CRLB-induced Bound (CRIB) for Interference-to-Signal Ratio (ISR) is derived. Numerical simulations compare the CRIB with the performance of an ICA and an ICE algorithm. The results show good agreement between the theory and the empirical results.

关键词： Ice Mathematical model Probability density function Conferences Electronic mail computational modeling Adaptation models

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Nonlinear system identification: finding structure in nonlinear black-box models 7

Nonlinear system identification: finding structure in nonlin...

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7th ieee international workshop on computational advances in multi-sensor adaptive processing (CAMSAP)

作者： Dreesen, Philippe Tiels, Koen Ishteva, Mariya Schoukens, Johan VUB Pleinlaan 2 B-1050 Brussels Belgium

ISBN: (纸本)9781538612514

The use of black-box models is wide-spread in signal processing and system identification applications. However, often such models possess a large number of parameters, and obfuscate their inner workings, as there are cross-connections between all inputs and all outputs (and possibly all internal states) of the model. Although black-box models have proven their success and wide applicability, there is a need to shed a light on what goes on inside the model. We have developed a tensor-based method that aims at pinpointing the nonlinearities of a given nonlinear model into a small number of univariate nonlinear mappings, with the advantageous side-effect of reducing the parametric complexity. In this contribution we will discuss how the method is conceived, and how it can be applied to the task of finding structure in black box models. We have found that the tensor-based decoupling method is able to reconstruct up to high accuracy a given black box nonlinear model, while reducing the parametric complexity and revealing some of the inner operation of the model. Due to their universal use, we will focus the presentation on the use of nonlinear state-space models, but the method is also suitable for other model structures. We validate the method on a case study in nonlinear system identification.

关键词： State-space methods Jacobian matrices Complexity theory Adaptation models Mathematical model Tensile stress

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