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Image Processing, IEEE Transactions on

Issue 1 • Date Jan. 2009

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Displaying Results 1 - 25 of 28
  • Table of contents

    Publication Year: 2009 , Page(s): C1
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    Freely Available from IEEE
  • IEEE Transactions on Image Processing publication information

    Publication Year: 2009 , Page(s): C2
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  • A Skeleton Family Generator via Physics-Based Deformable Models

    Publication Year: 2009 , Page(s): 1 - 11
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (588 KB) |  | HTML iconHTML  

    This paper presents a novel approach for object skeleton family extraction. The introduced technique utilizes a 2-D physics-based deformable model that parameterizes the objects shape. Deformation equations are solved exploiting modal analysis, and proportional to model physical characteristics, a different skeleton is produced every time, generating, in this way, a family of skeletons. The theoretical properties and the experiments presented demonstrate that obtained skeletons match to hand-labeled skeletons provided by human subjects, even in the presence of significant noise and shape variations, cuts and tears, and have the same topology as the original skeletons. In particular, the proposed approach produces no spurious branches without the need of any known skeleton pruning method. View full abstract»

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  • Variational Bayesian Blind Deconvolution Using a Total Variation Prior

    Publication Year: 2009 , Page(s): 12 - 26
    Cited by:  Papers (34)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (5567 KB) |  | HTML iconHTML  

    In this paper, we present novel algorithms for total variation (TV) based blind deconvolution and parameter estimation utilizing a variational framework. Using a hierarchical Bayesian model, the unknown image, blur, and hyperparameters for the image, blur, and noise priors are estimated simultaneously. A variational inference approach is utilized so that approximations of the posterior distributions of the unknowns are obtained, thus providing a measure of the uncertainty of the estimates. Experimental results demonstrate that the proposed approaches provide higher restoration performance than non-TV-based methods without any assumptions about the unknown hyperparameters. View full abstract»

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  • Image Sequence Denoising via Sparse and Redundant Representations

    Publication Year: 2009 , Page(s): 27 - 35
    Cited by:  Papers (66)  |  Patents (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2813 KB) |  | HTML iconHTML  

    In this paper, we consider denoising of image sequences that are corrupted by zero-mean additive white Gaussian noise. Relative to single image denoising techniques, denoising of sequences aims to also utilize the temporal dimension. This assists in getting both faster algorithms and better output quality. This paper focuses on utilizing sparse and redundant representations for image sequence denoising. In the single image setting, the K-SVD algorithm is used to train a sparsifying dictionary for the corrupted image. This paper generalizes the above algorithm by offering several extensions: i) the atoms used are 3-D; ii) the dictionary is propagated from one frame to the next, reducing the number of required iterations; and iii) averaging is done on patches in both spatial and temporal neighboring locations. These modifications lead to substantial benefits in complexity and denoising performance, compared to simply running the single image algorithm sequentially. The algorithm's performance is experimentally compared to several state-of-the-art algorithms, demonstrating comparable or favorable results. View full abstract»

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  • Generalizing the Nonlocal-Means to Super-Resolution Reconstruction

    Publication Year: 2009 , Page(s): 36 - 51
    Cited by:  Papers (148)  |  Patents (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (7177 KB) |  | HTML iconHTML  

    Super-resolution reconstruction proposes a fusion of several low-quality images into one higher quality result with better optical resolution. Classic super-resolution techniques strongly rely on the availability of accurate motion estimation for this fusion task. When the motion is estimated inaccurately, as often happens for nonglobal motion fields, annoying artifacts appear in the super-resolved outcome. Encouraged by recent developments on the video denoising problem, where state-of-the-art algorithms are formed with no explicit motion estimation, we seek a super-resolution algorithm of similar nature that will allow processing sequences with general motion patterns. In this paper, we base our solution on the Nonlocal-Means (NLM) algorithm. We show how this denoising method is generalized to become a relatively simple super-resolution algorithm with no explicit motion estimation. Results on several test movies show that the proposed method is very successful in providing super-resolution on general sequences. View full abstract»

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  • Subband Weighting With Pixel Connectivity for 3-D Wavelet Coding

    Publication Year: 2009 , Page(s): 52 - 62
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1247 KB) |  | HTML iconHTML  

    Performing optimal bit-allocation with 3-D wavelet coding methods is difficult because energy is not conserved after applying the motion-compensated temporal filtering (MCTF) process and the spatial wavelet transform. The problem cannot be solved by extending the 2-D wavelet coefficients weighting method directly and then applying the result to 3-D wavelet coefficients, since this approach does not consider the complicated pixel connectivity that results from the lifting-based MCTF process. In this paper, we propose a novel weighting method, which takes account of the pixel connectivity, to solve the problem and derive the effect of the quantization error of a subband on the reconstruction error of a group of pictures. We employ the proposed method on a 2-D+t structure with different temporal filters, namely the 5-3 filter and the 9-7 filter. Experiments on various coding parameters and sequences show that the proposed approach improves the bit-allocation performance over that obtained by using the weightings derived without considering the pixel connectivity in the MCTF process. View full abstract»

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  • Joint Optimization of Run-Length Coding, Huffman Coding, and Quantization Table With Complete Baseline JPEG Decoder Compatibility

    Publication Year: 2009 , Page(s): 63 - 74
    Cited by:  Papers (12)  |  Patents (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (682 KB) |  | HTML iconHTML  

    To maximize rate distortion performance while remaining faithful to the JPEG syntax, the joint optimization of the Huffman tables, quantization step sizes, and DCT indices of a JPEG encoder is investigated. Given Huffman tables and quantization step sizes, an efficient graph-based algorithm is first proposed to find the optimal DCT indices in the form of run-size pairs. Based on this graph-based algorithm, an iterative algorithm is then presented to jointly optimize run-length coding, Huffman coding, and quantization table selection. The proposed iterative algorithm not only results in a compressed bitstream completely compatible with existing JPEG and MPEG decoders, but is also computationally efficient. Furthermore, when tested over standard test images, it achieves the best JPEG compression results, to the extent that its own JPEG compression performance even exceeds the quoted PSNR results of some state-of-the-art wavelet-based image coders such as Shapiro's embedded zerotree wavelet algorithm at the common bit rates under comparison. Both the graph-based algorithm and the iterative algorithm can be applied to application areas such as web image acceleration, digital camera image compression, MPEG frame optimization, and transcoding, etc. View full abstract»

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  • Down-Sampling Design in DCT Domain With Arbitrary Ratio for Image/Video Transcoding

    Publication Year: 2009 , Page(s): 75 - 89
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4908 KB) |  | HTML iconHTML  

    This paper proposes a designing framework for down-sampling compressed images/video with arbitrary ratio in the discrete cosine transform (DCT) domain. In this framework, we first derive a set of DCT-domain down-sampling methods which can be represented by a linear transform with double-sided matrix multiplication (LTDS) in the DCT domain and show that the set contains a wide range of methods with various complexity and visual quality. Then, for a preselected spatial-domain down-sampling method, we formulate an optimization problem for finding an LTDS to approximate the given spatial-domain down-sampling method for a trade-off between the visual quality and the complexity. By modeling LTDS as a multiple layer network, a so-called structural learning with forgetting algorithm is then applied to solve the optimization problem. The proposed framework has been applied to discover optimal LTDSs corresponding to a spatial down-sampling method with Butterworth low-pass filtering and bicubic interpolation. Experimental results show that the resulting LTDS achieves a significant reduction on the complexity when compared with other methods in the literature with similar visual quality. View full abstract»

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  • Joint Source-Channel Distortion Modeling for MPEG-4 Video

    Publication Year: 2009 , Page(s): 90 - 105
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1587 KB) |  | HTML iconHTML  

    Multimedia communication has become one of the main applications in commercial wireless systems. Multimedia sources, mainly consisting of digital images and videos, have high bandwidth requirements. Since bandwidth is a valuable resource, it is important that its use should be optimized for image and video communication. Therefore, interest in developing new joint source-channel coding (JSCC) methods for image and video communication is increasing. Design of any JSCC scheme requires an estimate of the distortion at different source coding rates and under different channel conditions. The common approach to obtain this estimate is via simulations or operational rate-distortion curves. These approaches, however, are computationally intensive and, hence, not feasible for real-time coding and transmission applications. A more feasible approach to estimate distortion is to develop models that predict distortion at different source coding rates and under different channel conditions. Based on this idea, we present a distortion model for estimating the distortion due to quantization and channel errors in MPEG-4 compressed video streams at different source coding rates and channel bit error rates. This model takes into account important aspects of video compression such as transform coding, motion compensation, and variable length coding. Results show that our model estimates distortion within 1.5 dB of actual simulation values in terms of peak-signal-to-noise ratio. View full abstract»

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  • Accurate Distortion Estimation and Optimal Bandwidth Allocation for Scalable H.264 Video Transmission Over MIMO Systems

    Publication Year: 2009 , Page(s): 106 - 116
    Cited by:  Papers (14)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (593 KB) |  | HTML iconHTML  

    In this paper, we propose an optimal strategy for the transmission of scalable video over packet-based multiple-input multiple-output (MIMO) systems. The scalable extension of H.264/AVC that provides a combined temporal, quality and spatial scalability is used. For given channel conditions, we develop a method for the estimation of the distortion of the received video and propose different error concealment schemes. We show the accuracy of our distortion estimation algorithm in comparison with simulated wireless video transmission with packet errors. In the proposed MIMO system, we employ orthogonal space-time block codes (O-STBC) that guarantee independent transmission of different symbols within the block code. In the proposed constrained bandwidth allocation framework, we use the estimated end-to-end decoder distortion to optimally select the application layer parameters, i.e., quantization parameter (QP) and group of pictures (GOP) size, and physical layer parameters, i.e., rate-compatible turbo (RCPT) code rate and symbol constellation. Results show the substantial performance gain by using different symbol constellations across the scalable layers as compared to a fixed constellation. View full abstract»

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  • Event-by-Event Image Reconstruction From List-Mode PET Data

    Publication Year: 2009 , Page(s): 117 - 124
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1858 KB) |  | HTML iconHTML  

    This paper adapts the classical list-mode OSEM and the globally convergent list-mode COSEM methods to the special case of singleton subsets. The image estimate is incrementally updated for each coincidence event measured by the PET scanner. Events are used as soon as possible to improve the current image estimate, and, therefore, the convergence speed toward the maximum-likelihood solution is accelerated. An alternative online formulation of the list-mode COSEM algorithm is proposed first. This method saves memory resources by re-computing previous incremental image contributions while processing a new pass over the complete dataset. This online expectation-maximization principle is applied to the list-mode OSEM method, as well. Image reconstructions have been performed from a simulated dataset for the NCAT torso phantom and from a clinical dataset. Results of the classical and event-by-event list-mode algorithms are discussed in a systematic and quantitative way. View full abstract»

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  • A Unifying Approach to Moment-Based Shape Orientation and Symmetry Classification

    Publication Year: 2009 , Page(s): 125 - 139
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2751 KB) |  | HTML iconHTML  

    In this paper, the problem of moment-based shape orientation and symmetry classification is jointly considered. A generalization and modification of current state-of-the-art geometric moment-based functions is introduced. The properties of these functions are investigated thoroughly using Fourier series analysis and several observations and closed-form solutions are derived. We demonstrate the connection between the results presented in this work and symmetry detection principles suggested from previous complex moment-based formulations. The proposed analysis offers a unifying framework for shape orientation/symmetry detection. In the context of symmetry classification and matching, the second part of this work presents a frequency domain method, aiming at computing a robust moment-based feature set based on a true polar Fourier representation of image complex gradients and a novel periodicity detection scheme using subspace analysis. The proposed approach removes the requirement for accurate shape centroid estimation, which is the main limitation of moment-based methods, operating in the image spatial domain. The proposed framework demonstrated improved performance, compared to state-of-the-art methods. View full abstract»

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  • Correspondence Propagation with Weak Priors

    Publication Year: 2009 , Page(s): 140 - 150
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1902 KB) |  | HTML iconHTML  

    For the problem of image registration, the top few reliable correspondences are often relatively easy to obtain, while the overall matching accuracy may fall drastically as the desired correspondence number increases. In this paper, we present an efficient feature matching algorithm to employ sparse reliable correspondence priors for piloting the feature matching process. First, the feature geometric relationship within individual image is encoded as a spatial graph, and the pairwise feature similarity is expressed as a bipartite similarity graph between two feature sets; then the geometric neighborhood of the pairwise assignment is represented by a categorical product graph, along which the reliable correspondences are propagated; and finally a closed-form solution for feature matching is deduced by ensuring the feature geometric coherency as well as pairwise feature agreements. Furthermore, our algorithm is naturally applicable for incorporating manual correspondence priors for semi-supervised feature matching. Extensive experiments on both toy examples and real-world applications demonstrate the superiority of our algorithm over the state-of-the-art feature matching techniques. View full abstract»

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  • Improvements in Shape-From-Focus for Holographic Reconstructions With Regard to Focus Operators, Neighborhood-Size, and Height Value Interpolation

    Publication Year: 2009 , Page(s): 151 - 157
    Cited by:  Papers (16)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2036 KB) |  | HTML iconHTML  

    This paper presents a shape-from-focus method, which is improved with regard to the mathematical operator used for contrast measurement, the selection of the neighborhood size, surface refinement through interpolation, and surface postprocessing. Three-dimensional models of living human faces are presented with such a high resolution that single hairs are visible. View full abstract»

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  • Independent Component Analysis-Based Background Subtraction for Indoor Surveillance

    Publication Year: 2009 , Page(s): 158 - 167
    Cited by:  Papers (51)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1920 KB) |  | HTML iconHTML  

    In video surveillance, detection of moving objects from an image sequence is very important for target tracking, activity recognition, and behavior understanding. Background subtraction is a very popular approach for foreground segmentation in a still scene image. In order to compensate for illumination changes, a background model updating process is generally adopted, and leads to extra computation time. In this paper, we propose a fast background subtraction scheme using independent component analysis (ICA) and, particularly, aims at indoor surveillance for possible applications in home-care and health-care monitoring, where moving and motionless persons must be reliably detected. The proposed method is as computationally fast as the simple image difference method, and yet is highly tolerable to changes in room lighting. The proposed background subtraction scheme involves two stages, one for training and the other for detection. In the training stage, an ICA model that directly measures the statistical independency based on the estimations of joint and marginal probability density functions from relative frequency distributions is first proposed. The proposed ICA model can well separate two highly-correlated images. In the detection stage, the trained de-mixing vector is used to separate the foreground in a scene image with respect to the reference background image. Two sets of indoor examples that involve switching on/off room lights and opening/closing a door are demonstrated in the experiments. The performance of the proposed ICA model for background subtraction is also compared with that of the well-known FastICA algorithm. View full abstract»

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  • Estimation of Motions in Color Image Sequences Using Hypercomplex Fourier Transforms

    Publication Year: 2009 , Page(s): 168 - 187
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1844 KB) |  | HTML iconHTML  

    Although the motion estimation problem has been extensively studied, most of the proposed estimation approaches deal mainly with monochrome videos. The most usual way to apply them also in color image sequences is to process each color channel separately. A different, more sophisticated approach is to process the color channels in a ldquoholisticrdquo manner using quaternions, as proposed by Ell and Sangwine. In this paper, we extend standard spatiotemporal Fourier-based approaches to handle color image sequences, using the hypercomplex Fourier transform. We show that translational motions are manifested as energy concentration along planes in the hypercomplex 3D Fourier domain and we describe a methodology to estimate the motions, based on this property. Furthermore, we compare the three-channels-separately approach with our approach and we show that the computational effort can be reduced by a factor of 1/3, using the hypercomplex Fourier transform. Also, we propose a simple, accompanying method to extract the moving objects in the hypercomplex Fourier domain. Our experimental results on synthetic and natural images verify our arguments throughout the paper. View full abstract»

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  • A Sparsity-Enforcing Method for Learning Face Features

    Publication Year: 2009 , Page(s): 188 - 201
    Cited by:  Papers (14)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2008 KB) |  | HTML iconHTML  

    In this paper, we propose a new trainable system for selecting face features from over-complete dictionaries of image measurements. The starting point is an iterative thresholding algorithm which provides sparse solutions to linear systems of equations. Although the proposed methodology is quite general and could be applied to various image classification tasks, we focus here on the case study of face and eyes detection. For our initial representation, we adopt rectangular features in order to allow straightforward comparisons with existing techniques. For computational efficiency and memory saving requirements, instead of implementing the full optimization scheme on tenths of thousands of features, we propose a three-stage architecture which consists of finding first intermediate solutions to smaller size optimization problems, then merging the obtained results, and next applying further selection procedures. The devised system requires the solution of a number of independent problems, and, hence, the necessary computations could be implemented in parallel. Experimental results obtained on both benchmark and newly acquired face and eyes images indicate that our method is a serious competitor to other feature selection schemes recently popularized in computer vision for dealing with problems of real-time object detection. A major advantage of the proposed system is that it performs well even with relatively small training sets. View full abstract»

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  • Synchronized Submanifold Embedding for Person-Independent Pose Estimation and Beyond

    Publication Year: 2009 , Page(s): 202 - 210
    Cited by:  Papers (17)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1055 KB) |  | HTML iconHTML  

    Precise 3-D head pose estimation plays a significant role in developing human-computer interfaces and practical face recognition systems. This task is challenging due to the particular appearance variations caused by pose changes for a certain subject. In this paper, the pose data space is considered as a union of submanifolds which characterize different subjects, instead of a single continuous manifold as conventionally regarded. A novel manifold embedding algorithm dually supervised by both identity and pose information, called snchronized submanifold embedding (SSE), is proposed for person-independent precise 3-D pose estimation, which means that the testing subject may not appear in the model training stage. First, the submanifold of a certain subject is approximated as a set of simplexes constructed using neighboring samples. Then, these simplexized submanifolds from different subjects are embedded by synchronizing the locally propagated poses within the simplexes and at the same time maximizing the intrasubmanifold variances. Finally, the pose of a new datum is estimated as the propagated pose of the nearest point within the simplex constructed by its nearest neighbors in the dimensionality reduced feature space. The experiments on the 3-D pose estimation database, CHIL data for CLEAR07 evaluation, and the extended application for age estimation on FG-NET aging database, demonstrate the superiority of SSE over conventional regression algorithms as well as unsupervised manifold learning algorithms. View full abstract»

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  • Improved Block Truncation Coding Based on the Void-and-Cluster Dithering Approach

    Publication Year: 2009 , Page(s): 211 - 213
    Cited by:  Papers (18)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1470 KB) |  | HTML iconHTML  

    Block truncation coding (BTC) is an efficient technology for image compression. An improved BTC algorithm, namely ordered dither block truncation coding (ODBTC), is presented in this study. In order to provide better image quality, the void-and-cluster halftoning is combined with the BTC. The ODBTC results show that the image quality is improved when it is operated in high coding gain applications. Another feature of the ODBTC is the dither array look up table (LUT), which significantly reduces the complexity compared to the BTC. View full abstract»

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  • Comments on "Optimal Erasure Protection for Scalably Compressed Video Streams With Limited Retransmission

    Publication Year: 2009 , Page(s): 214 - 216
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (109 KB) |  | HTML iconHTML  

    In order to prove a key result for their development (Lemma 2), Taubman and Thie need the assumption that the upper boundary of the convex hull of the channel coding probability-redundancy characteristic is sufficiently dense. Since a floor value for the density level for which the claim to hold is not specified, it is not clear whether their lemma applies to practical situations. In this correspondence, we show that the constraint of sufficient density can be removed, and, thus, we validate the conclusion of the lemma for any scenario encountered in practice. View full abstract»

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  • IEEE Transactions on Image Processing EDICS

    Publication Year: 2009 , Page(s): 217
    Save to Project icon | Request Permissions | PDF file iconPDF (20 KB)  
    Freely Available from IEEE
  • IEEE Transactions on Image Processing information for authors

    Publication Year: 2009 , Page(s): 218 - 219
    Save to Project icon | Request Permissions | PDF file iconPDF (46 KB)  
    Freely Available from IEEE
  • Call for papers

    Publication Year: 2009 , Page(s): 220
    Save to Project icon | Request Permissions | PDF file iconPDF (590 KB)  
    Freely Available from IEEE
  • Call for papers

    Publication Year: 2009 , Page(s): 221
    Save to Project icon | Request Permissions | PDF file iconPDF (522 KB)  
    Freely Available from IEEE

Aims & Scope

IEEE Transactions on Image Processing focuses on signal-processing aspects of image processing, imaging systems, and image scanning, display, and printing.

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Meet Our Editors

Editor-in-Chief
Scott Acton
University of Virginia
Charlottesville, VA, USA
E-mail: acton@virginia.edu 
Phone: +1 434-982-2003