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IEEE international workshop on Computational Advances in Multi-Sensor Adaptive processing (CAMSAP)

作者： Arash Mohammadi Amir Asif Computer Science and Engineering York University Toronto ONT Canada

In view of non-linear, non-Gaussian tracking applications in sensor networks, we propose a consensus/fusion based, distributed implementation of the particle filter (CF/DPF). The proposed distributed implementation addresses three important issues: (i) Extending the idea of the channel filters , separate fusion filters are designed to consistently assimilate the local filtering distributions into global posterior by compensating for the common past information between neighbouring nodes typically overlooked in consensus-based distributed particle filter implementations. (ii) The proposed method is not limited to Gaussian approximation for the global posterior density. (iii) Finally, the condition that the consensus step converges between two consecutive observations is partially relaxed. Our numerical simulations verify that the output of the CF/DPF is almost identical to that of the centralized particle filter.

关键词： Conferences Distributed databases Indexes Equations Trajectory Gaussian approximation Computers

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Collaborative learning of mixture models using diffusion adaptation

Collaborative learning of mixture models using diffusion ada...

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IEEE workshop on Machine Learning for Signal processing

作者： Zaid J. Towfic Jianshu Chen Ali H. Sayed Department of Electrical Engineering University of California Los Angeles USA

In large ad-hoc networks, classification tasks such as spam filtering, multi-camera surveillance, and advertising have been traditionally implemented in a centralized manner by means of fusion centers. These centers receive and process the information that is collected from across the network. In this paper, we develop a decentralized adaptive strategy for information processing and apply it to the task of estimating the parameters of a Gaussian-mixture-model (GMM). The proposed technique employs adaptive diffusion algorithms that enable adaptation, learning, and cooperation at local levels. The simulation results illustrate how the proposed technique outperforms non-collaborative learning and is competitive against centralized solutions.

关键词： Vectors Newton method Distributed databases Optimization Probability density function approximation methods Adaptation models

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Age estimation based on extended non-negative matrix factorization

Age estimation based on extended non-negative matrix factori...

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IEEE workshop on Multimedia Signal processing

作者： Ce Zhan Wanqing Li Philip Ogunbona School of Computer Science and Software Engineering University of Wollongong Australia

Previous studies suggested that local appearance-based methods are more efficient than geometric-based and holistic methods for age estimation. This is mainly due to the fact that age information are usually encoded by the local features such as wrinkles and skin texture on the forehead or at the eye corners. However, the variations of theses features caused by other factors such as identity, expression, pose and lighting may be larger than that caused by aging. Thus, one of the key challenges of age estimation lies in constructing a feature space that could successfully recovers age information while ignoring other sources of variations. In this paper, non-negative matrix factorization (NMF) is extended to learn a localized non-overlapping subspace representation for age estimation. To emphasize the appearance variation in aging, one individual extended NMF subspace is learned for each age or age group. The age or age group of a given face image is then estimated based on its reconstruction error after being projected into the learned age subspaces. Furthermore, a coarse to fine scheme is employed for exact age estimation, so that the age is estimated within the pre-classified age groups. Cross-database tests are conducted using FG-NET and MORPH databases to evaluate the proposed method. Experimental results have demonstrated the efficacy of the method.

关键词： Face Estimation Databases Aging Active appearance model Testing image reconstruction

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A parallel resampling scheme and its application to distributed particle filtering in wireless networks

A parallel resampling scheme and its application to distribu...

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IEEE international workshop on Computational Advances in Multi-Sensor Adaptive processing (CAMSAP)

作者： Katrin Achutegui Joaquín Míguez Department of Signal Theory and Communications Universidad Carlos III de Madrid Leganes Spain

We address the design of a particle filter (PF) that can be implemented in a distributed manner over a network of wireless sensor nodes, each of them collecting their own local data. This is a problem that has received considerable attention lately and several methods based on consensus, the transmission of likelihood information, the truncation and/or the quantization of data have been proposed. However, all existing schemes suffer from limitations related either to the amount of required communications among the nodes or the accuracy of the filter outputs. In this work we propose a novel distributed PF that is built around the distributed resampling with non-proportional allocation (DRNA) algorithm. This scheme guarantees the properness of the particle approximations produced by the filter and has been shown to be both efficient and accurate when compared with centralized PFs. The standard DRNA technique, however, places stringent demands on the communications among nodes that turn out impractical for a typical wireless sensor network (WSN). In this paper we investigate how to reduce this communication load by using (i) a random model for the spread of data over the WSN and (ii) methods that enable the out-of-sequence processing of sensor observations. A simple numerical illustration of the performance of the new algorithm compared with a centralized PF is provided.

关键词： Wireless sensor networks Artificial neural networks approximation algorithms Signal processing Markov processes Vectors approximation methods

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Irregular Tree-Structured Bayesian Network for image segmentation

Irregular Tree-Structured Bayesian Network for image segment...

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IEEE workshop on Machine Learning for Signal processing

作者： Kittipat Kampa Duangmanee Putthividhya Jose C. Principe Department of Electrical and Computer Engineering University of Florida Gainesville FL eBay Inc San Jose CA USA Department of Electrical and Computer Engineering University of Florida Gainesville FL USA

Unsupervised image segmentation algorithms rely heavily on a probabilistic smoothing prior to enforce local homogeneity in the segmentation results. The tree-structured prior [1, 2, 3] is one such prior which allows important multi-scale spatial correlations that exist in natural images to be captured. Two main types of tree structure prior have been previously proposed: 1) fixed quadtree structure [1], which suffers from “blockiness” in the segmentation results and 2) flexible tree structure [2, 3] which can adapt its structure to the natural object boundary but at a significant computational cost. This paper presents a novel probabilistic unsupervised image segmentation framework called Irregular Tree-Structured Bayesian Networks (ITSBN) which introduces the notion of irregular tree structure that combines the merits of the two previous approaches. As in [2, 3], more natural object boundaries can be modeled in our framework since a tree is learned for each input image. Our method, however, does not update the adaptive structure at every iteration which drastically reduces the computation required. We derive a time-efficient exact inference algorithm based on a sum-product framework using factor graphs [4]. Furthermore, a novel methodology for the evaluation of unsupervised image segmentation is proposed. By integrating non-parametric density estimation techniques with the traditional precision-recall framework, the proposed method is more robust to boundary inconsistency due to human subjects.

关键词： image segmentation Bayesian methods Inference algorithms Graphical models image color analysis Computational modeling Adaptation models

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Color constancy and non-uniform illumination: Can existing algorithms work?

Color constancy and non-uniform illumination: Can existing a...

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international Conference on Computer Vision workshops (ICCV workshops)

作者： Michael Bleier Christian Riess Shida Beigpour Eva Eibenberger Elli Angelopoulou Tobias Tröger André Kaup Pattern Recognition Lab University of Erlangen-Nuremberg Germany Computer Vision Center Universidad Autónoma de Barcelona Spain Multimedia Communications and Signal Processing University of Erlangen-Nuremberg Germany

The color and distribution of illuminants can significantly alter the appearance of a scene. The goal of color constancy (CC) is to remove the color bias introduced by the illuminants. Most existing CC algorithms assume a uniformly illuminated scene. However, more often than not, this assumption is an insufficient approximation of real-world illumination conditions (multiple light sources, shadows, interreflections, etc.). Thus, illumination should be locally determined, taking under consideration that multiple illuminants may be present. In this paper we investigate the suitability of adapting 5 state-of-the-art color constancy methods so that they can be used for local illuminant estimation. Given an arbitrary image, we segment it into superpixels of approximately similar color. Each of the methods is applied independently on every superpixel. For improved accuracy, these independent estimates are combined into a single illuminant-color value per superpixel. We evaluated different fusion methodologies. Our experiments indicate that the best performance is obtained by fusion strategies that combine the outputs of the estimators using regression.

关键词： image color analysis Lighting Databases Light sources image segmentation Estimation Bayesian methods

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An Efficient Algorithm for non-Rigid image Registration

An Efficient Algorithm for Non-Rigid Image Registration

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4th international Conference on Signal processing and Communication Systems (ICSPCS)/12th international Symposium on DSP and Communication Systems (DSPCS)/9th workshop on the Internet, Telecommunications and Signal processing (WTSP)

作者： Wang, Guanglei Lui, Hoi-Shun Persson, Mikael Chalmers Univ Technol Dept Signals & Syst S-41296 Gothenburg Sweden

ISBN: (纸本)9781424479078

An efficient algorithm for non-rigid image registration is proposed. The proposed method consists of two-stage registration process, which is based on affine transformation in both local and global registration. Numerical examples demonstrated that the proposed method provides more accurate registration results than global registration methods.

关键词： affine transformation affine transforms global registration image registration local registration nonrigid image registration

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Variational Bayesian inference image restoration using a product of total variation-like image priors

Variational Bayesian inference image restoration using a pro...

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international workshop on Cognitive Information processing

作者： Chantas, Giannis Galatsanos, Nikolaos Molina, Rafael Katsaggelos, Aggelos Department of Computer Science University of Ioannina Ioannina 45110 Greece Department of Electrical and Computer Engineering University of Patras Rio 26500 Greece Department of Computer Science University of Granada Granada 18071 Spain Department of Electrical and Computer Engineering Northwestern University Evanston IL 60208-3118 United States

ISBN: (纸本)9781424464593

In this paper a new image prior is introduced and used in image restoration. This prior is based on products of spatially weighted Total Variations (TV). These spatial weights provide this prior with the flexibilit to better capture local image features than previous TV based priors. Bayesian inference is used for image restoration with this prior via the variational approximation. The proposed algorithm is fully automatic in the sense that all necessary parameters are estimated from the data. Numerical experiments are shown which demonstrate that image restoration based on this prior compares favorably with previous state-of-the-art restoration algorithms. © 2010 IEEE.

关键词： image reconstruction

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Proceedings of the 2010 IEEE international workshop on Machine Learning for Signal processing, MLSP 2010

Proceedings of the 2010 IEEE International Workshop on Machi...

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2010 IEEE 20th international workshop on Machine Learning for Signal processing, MLSP 2010

ISBN: (纸本)9781424478774

The proceedings contain 82 papers. The topics discussed include: mutual information based dimensionality reduction with application to non-linear regression;functional data representation using correntropy locally linear embedding;local dimensionality reduction for multiple instance learning;multiplicative updates for t-SNE;manifold-respecting probabilistic matrix tri-factorization;single-frame image super-resolution unising a pearson type VII MRF;a one-pass resource-allocating codebook for patch-based visual object recognition;learning spatial filters for multispectral image segmentation;improperness measures for quaternion random vectors;Bayesian BCJR for channel equalization and decoding;reinforcement learning method for energy efficient cooperative multiband spectrum sensing;and error-related potential recorded by EEG in the context of a P300 mind speller brain-computer interface.

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Imaging from sparse measurements

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GEOPHYSICAL JOURNAL international 2010年第3期180卷 1289-1302页

作者： Fang, Y. Cheney, M. Roecker, S. Rensselaer Polytech Inst Troy NY 12180 USA

We consider the problem of using scattered waves to recover an image of the medium in which the waves propagate. We address the case of scalar waves when the sources and receivers are sparse and irregularly spaced. Our approach is based on the single-scattering (Born) approximation and the generalized Radon transform. The key to handling sparse sources and receivers is the development of a data-weighting scheme that compensates for non-uniform sampling. To determine the appropriate weights, we formulate a criterion for measuring the optimality of the point-spread function, and solve the resulting optimization problem using regularized least squares. Once the weights are determined, they can be used to compute the point-spread function and thus determine resolution, and they can also be applied to the measured data to form an image. Tests of our minimization scheme with different regularization parameters show that, with appropriate weighting, individual scatterers can be resolved at subwavelength scales even when data is noisy and the locations of both sources and receivers are uncertain. We show an example in which the source-receiver geometry and frequency bandwidths correspond to seismic imaging from multiple local earthquakes (passive seismic imaging). The example shows that the weights determined by our method improve the resolution relative to reconstructions with constant weights.

关键词： image processing Fourier analysis Inverse theory Seismic tomography Wave scattering and diffraction

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