News from participating projectsHere lists the interesting papers/tools/research ideas I found recently. As I also post these references to my participating projects, the right sidebar show RSSs to these projects' site for these posts. Vis  Graphics
 Computer Vision / Image processing
 HPC  Submission deadline

 SIGGRAPH '15

  Jan. 19, 2015
 IEEE Vis '15


  Mar. 21/Mar. 31, 2015    ACM MM '15
  Mar. 31, 2015
    SC '15
 Apr. 3/Apr. 10, 2015
   ACM MM '15 Short paper
  Apr. 30, 2015
    LDAV '15
 May 14, 2014

 PG '14 
  May 30/May 31, 2014 
 SIGGRAPH Asia '14 
  June 3, 2014  PacificVis '15 

  Sep. 26, 2014     IPDPS '15
 Oct. 10/OCt. 17, 2014

 I3D '15

  Oct. 21, 2014    CVPR '15
  Nov. 14, 2014
 EuroVis '15 

  Nov. 30/Dec. 05, 2014 

posted Sep 30, 2014, 5:05 PM by TengYok Lee
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updated Oct 10, 2014, 2:23 PM
]
For neural network. http://nxxcxx.github.io/NeuralNetwork/
Inviso: Visualization Hadoop performance by Netflix. https://github.com/Netflix/inviso
VisNEST  Visualization of Simulated Neural Brain Activity http://www.jara.org/en/research/jarahpc/research/details/csgimmersivevis/visnestvisualizationofsimulatedneuralbrainactivity/
Visualizing MNIST: An Exploration of Dimensionality Reduction https://colah.github.io/posts/201410VisualizingMNIST/
Opening the black box  data driven visualization of neural networksF.Y. Tzeng and KwanLiu Ma Proceedings of IEEE Visualization 2005, pp.383,390, 2328 Oct. 2005 pdf

posted Sep 30, 2014, 8:29 AM by TengYok Lee
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updated Sep 30, 2014, 8:29 AM
]
These papers are the suggestion toread list of the Coursera course Discrete Inference and Learning in Artificial Vision (instructors: Nikos Paragios and Pawan Kumar) https://www.coursera.org/course/artificialvision Authors  Title  Book
 Chaohui Wang, Nikos Komodakis, Nikos Paragios  Markov Random Field
modeling, inference & learning in computer vision & image understanding:
A survey  Computer Vision and Image Understanding 117(11): 16101627
(2013)  Yuri Boykov and Vladimir Kolmogorov  An Experimental Comparison of
MinCut/MaxFlow Algorithms for Energy Minimization in Vision  IEEE
Transactions on Pattern Analysis and Machine Intelligence,
26(9):11241137 (2004)  Yuri Boykov and Olga Veksler  Graph Cuts in Vision and Graphics: Theories and Applications  Handbook of Mathematical Models in Computer Vision, edited by Nikos Paragios, Yunmei Chen and Olivier Faugeras. Springer, 2006  Yuri Boykov, Olga Veksler and Ramin Zabih  Fast Approximate Energy
Minimization via Graph Cuts  IEEE Transactions on Pattern Analysis
and Machine Inteligence, 23(11): 12221239 (2001)  Vladimir Kolmogorov  Convergent Treereweighted Message Passing for
Energy Minimization  IEEE Transactions on Pattern Analysis and
Machine Intelligence 28(10): 15681583 (2006)  Vladimir Kolmogorov and Ramin Zabih  What Energy Functions can be
Minimized via Graph Cuts?  IEEE Transactions on Pattern Analysis and
Machine Inteligence, 26(2): 147159 (2004)  Nikos Komodakis, Georgios Tziritas, Nikos Paragios  Performance
vs computational efficiency for optimizing single and dynamic MRFs:
Setting the state of the art with primaldual strategies  Computer
Vision and Image Understanding 112(1), 1429 (2008)  Nikos Komodakis, Nikos Paragios, Georgios Tziritas  MRF Energy Minimization and Beyond via Dual Decomposition  IEEE Transactions on Pattern Analysis Machine Intelligence 33(3): 531552 (2011)  Ben Taskar, Carlos Guestrin and Daphne Koller  MaxMargin Markov
Networks  Proceedings of Advances in Neural Information Processing
Systems: (2003)  Ioannis Tsochantaridis, Thorsten Joachims, Thomas Hofmann and Yasmine
Altun  Large Margin Methods for Structured and Interdependent Output
Variables  Journal of Machine Learning Research, 6:14531484 (2005)  Tomas Werner  Revisiting the Linear Programming Relaxation Approach to
Gibbs Energy Minimization and Weighted Constraint Satisfaction  IEEE
Transactions on Pattern Analysis and Machine Intelligence 32(8): 14741488 (2010) 

posted Sep 29, 2014, 9:42 PM by TengYok Lee
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updated Oct 3, 2014, 7:05 PM
]
Explorable images for visualizing volume data.
Anna Tikhonova, Carlos D. Correa, KwanLiu Ma. In Proceedings of IEEE Pacific Visualization Symposium 2010, pp. 177184, 2010. An Exploratory Technique for Coherent Visualization of Timevarying Volume Data.Anna Tikhonova, Carlos D. Correa, KwanLiu Ma. Computer Graphics Forum, 29(3):783792, 2010. pdf Visualization by Proxy: A Novel Framework for Deferred Interaction with Volume Data. Anna Tikhonova, Carlos D. Correa, KwanLiu Ma. IEEE Transactions on Visualization and Computer Graphics, 16(6):15511559, 2010.
An Imagebased Approach to Extreme Scale In Situ Visualization and Analysis. James Ahrens, John Patchett, Sebastien Jourdain, David H. Rogers, Patrick O’Leary, and Mark Petersen. SuperComputing 2014, to appear.
PS. Another set of relevant paper is to evaluate the visibility of a single view point:
VisibilityDriven Transfer Functions. Carlos D. Correa, KwanLiu Ma. In Proceedings of IEEE Pacific Visualization Symposium 2009, 2009.
Visibility Histograms and VisibilityDriven Transfer Functions. Carlos D. Correa, KwanLiu Ma. IEEE Transactions on Visualization and Computer Graphics, 17(2):192204, 2011.

posted Sep 27, 2014, 10:12 AM by TengYok Lee
An article to refresh my memory on statistics? Motulsky HJ, Common Misconceptions about Data Analysis and Statistics. J Pharmacol Exp Ther. 351(1):2005, 2014 Oct. doi: 10.1124/jpet.114.219170. http://jpet.aspetjournals.org/content/351/1/200.full.pdf 
posted Sep 7, 2014, 11:07 PM by TengYok Lee
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updated Sep 24, 2014, 5:25 PM
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REF: http://ieeevis.org/year/2014/info/overviewamptopics/acceptedpapers
SciVisCharacterizing Molecular Interactions in Chemical Systems David Guenther, Roberto Alvarez Boto, Julia Contreras Garcia, JeanPhilip Piquemal, Julien Tierny Vortex Cores of Inertial ParticlesTobias Günther, Holger Theisel AdvectionBased Sparse Data Management for Visualizing Unsteady FlowHanqi Guo, Jiang Zhang, Richen Liu, Lu Liu, Xiaoru Yuan, Jian Huang, Xiangfei Meng, Jingshan Pan FLDA: Latent Dirichlet Allocation Based Unsteady Flow Analysis Fan Hong, Chufan Lai, Hanqi Guo, Xiaoru Yuan, Enya Shen, Sikun Li FixedRate Compressed FloatingPoint Arrays Peter Lindstrom Sparse PDF Volumes for Consistent MultiResolution Volume Rendering Ronell Sicat, Jens Krueger, Torsten Möller, Markus Hadwiger
preprint
TVCG InterpolationBased Pathline Tracing in ParticleBased Flow Visualization Jennifer Chandler, Harald Obermaier, Ken Joy

posted Jun 9, 2014, 10:32 PM by TengYok Lee
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updated Jun 10, 2014, 8:59 AM
]
REF: http://www.holehouse.org/mlclass/06_Logistic_Regression.html
Because it took me a while to finally derive it, I decide to put the detail here. Since I cannot type the equation nice, I simplify the notations.
Note 1: Gradients of logistic cost functions
Here the cost function is denoted as F(t) where t terms for theta. m is the number of samples. (x^{(i)}, y^{(i)}), i = 1 ... m, is the training set. h_{t}(x) =1/(1+exp(t^{T}x)) is the logistic function with parameter theta (t).
F ( t ) = 1/ m sum _{i = 1 ... m} y ^{(i)} log h _{t}( x ^{(i)}) + (1  y ^{(i)})log (1  h _{t}( x ^{(i)}))
The partial gradient w.tr.t t_{j} is denoted as d/d t_{j} = d _{j}. Then the partial gradient at t _{j}, aka, d _{j} F( t) is derived as follows: d _{j} F ( t )
= 1/ m sum _{i = 1 ... m} y ^{(i)} / h _{t}( x ^{(i)}) d _{j} h _{t}( x ^{(i)}) + (1  y ^{(i)})/(1  h _{t}( x ^{(i)})) d _{j} (1  h _{t}( x ^{(i)}))
= 1/ m sum _{i = 1 ... m} y ^{(i)} / h _{t}( x ^{(i)}) d _{j} h _{t}( x ^{(i)})  (1  y ^{(i)})/(1  h _{t}( x ^{(i)})) d _{j} h _{t}( x ^{(i)}) < Remove the constant 1 , which is in boldface above.
= 1/ m sum _{i = 1 ... m} d _{j} h _{t}( x ^{(i)}) { y ^{(i)} / h _{t}( x ^{(i)})  (1  y ^{(i)})/(1  h _{t}( x ^{(i)}))} < Separate d _{j} h _{t}( x ^{(i)}) from both terms .
= 1/ m sum _{i = 1 ... m} d _{j} h _{t}(x ^{(i)}) { y ^{(i)} (1  h _{t}( x ^{(i)}))  h _{t}( x ^{(i)})(1  y ^{(i)})}/{ h _{t}( x ^{(i)})(1  h _{t}( x ^{(i)})}
= 1/ m sum _{i = 1 ... m} d _{j} h _{t}(x ^{(i)}) ( y ^{(i)}  h _{t}( x ^{(i)}))/{ h _{t}( x ^{(i)})(1  h _{t}( x ^{(i)})}
where
d _{j} h _{t}( x ^{(i)})
= d _{j} (1 + exp( t ^{T}x ^{(i)})) ^{1}
= (1 + exp( t ^{T}x ^{(i)})) ^{2} exp( t ^{T}x ^{(i)}) x ^{(i)}_{j}
= {1/(1 + exp( t ^{T}x ^{(i)})} {exp( t ^{T}x ^{(i)}) / (1 + exp( t ^{T}x ^{(i)})} x ^{(i)}_{j}= { h _{t}( x ^{(i)})(1  h _{t}( x ^{(i)})} x ^{(i)}_{j}Thus d _{j} F ( t )
= 1/ m sum _{i = 1 ... m} d _{j} h _{t}( x ^{(i)}) ( y ^{(i)}  h _{t}( x ^{(i)}))/{ h _{t}( x ^{(i)})(1  h _{t}( x ^{(i)})}
= 1/ m sum _{i = 1 ... m} ( y ^{(i)}  h _{t}( x ^{(i)})) {{ h _{t}( x ^{(i)})(1  h _{t}( x ^{(i)})} x ^{(i)}_{j}}/{ h _{t}( x ^{(i)})(1  h _{t}( x ^{(i)})}
= 1/ m sum _{i = 1 ... m} ( h _{t}( x ^{(i)})  y ^{(i)}) x ^{(i)}_{j}
Note 2: How is logistic regression related to MLE?Actually the logistic regression cost can be treated as the likelihood of a Bernoulli random variable. Here P[ x; t] = h_{t}( x) is the probability that x belongs to class 0. Then the pdf of x is f ( x ) = P [ x ; t ] ^{y} (1  P [ x ; t ]) ^{1  y}
and its likelihood of theta t is:
L ( t ) = log f ( x ) = y log P [ x ; t ] + (1 y ) log 1  P [ x ; t ] = y log h _{t}( x ) + (1 y ) log 1  h _{t}( x )
That's why optimizing the cost function F is equivalent to find the Maximum Likelihood Estimator.

posted Jun 4, 2014, 10:50 PM by TengYok Lee
Research articlesVisualizing Largescale Parallel Communication Traces Using a Particle Animation Technique Carmen Sigovan, Chris Muelder, and KwanLiu Ma In Proceedings of EuroVis 2013. pdfGantt Chart Visualization for MPI and Apache MultiDimensional Trace FilesWu, C.E. and Bolmarcich, A.In IPDPS 2002. pdfSoftware packages 
posted Dec 20, 2013, 10:03 PM by TengYok Lee
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updated Feb 27, 2014, 6:41 PM
]
I am especially interested techniques that create effective overviews for videos: Techniques for interactive video cubism. S. S. Fels, E. Lee, and K. Mase. In MM ’00: Proceedings of ACM Multimedia 2000, pages 368–370, 2000. Video visualization. G. Daniel and M. Chen. In VIS ’03: Proceedings of the IEEE Visualization 2003, pages 409–416, 2003. Computational timelapse video. Eric P. Bennett and Leonard McMillan. In SIGGRAPH 2007, Article 102, 2007. Factored timelapse video. Kalyan Sunkavalli, Wojciech Matusik, Hanspeter Pfister, and Szymon Rusinkiewicz. In SIGGRAPH 2007, Article 101, 2007. Exploring video streams using slittear visualizations. A. Tang, S. Greenberg, and S. Fels. In AVI ’08: Proceedings of the Working Conference on Advanced Visual Interfaces 2008, pages 191–198, 2008. Actionbased multifield video visualization.
R. P. Botchen, S. Bachthaler, F. Schick, M. Chen, G. Mori, D. Weiskopf, and T. Ertl. IEEE Transactions on Visualization and Computer Graphics 14(4):885–899, 2008. Dynamic video narratives. Carlos D. Correa and KwanLiu Ma. In SIGGRAPH '10, Article
88 , 9 pages, 2010. 
posted Nov 9, 2013, 10:30 PM by TengYok Lee
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updated Nov 12, 2013, 1:20 AM
]
In computer vision, image processing, and computer graphics, the researchers have tried to optimize the computation of local value distribution since it can be applied to common image filtering such as median filtering and bilateral filtering. Recently, the researchers in the visualization society also study algorithms to efficiently query histograms for arbitrary regions and the applications of local distribution. This document lists papers about efficient computation of local distribution from Cartesian grids from these research areas.
This list will keep updating. So far (2013/11/11) my observation is that different area has different research interests about histogram.  The papers in computer graphics and image processing are more related to faster filtering for images/videos.
 The papers in information visualization are more related to fast query in order to query the histogram of arbitrary sub block in high dimensional data. (PS. so far I only know 2 papers from info vis researchers in recent years).
 The papers in scientific visualization are more related to the application side of local histograms. My hypothesis is that the researchers are looking for concrete examples how histograms can be applied for scientific visualization problems.
 On the other hand, scientific visualization conferences also have papers about fast histogram query. These papers are mainly from the research group of Prof. HanWei Shen.
Ppers in Computer Vision and Graphics avenues F. Porikli. Integral histogram: a fast way to extract histograms in cartesian spaces.In CVPR ’05: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, volume 1, pages 829 – 836, 2005. Ben Weiss. Fast median and bilateral filtering. In ACM SIGGRAPH 2006, pp. 519526, 2006. An O(log N) algorithm to quickly update the histogram per pixel. Based on the histograms, median filtering and bilateral filtering can be efficiently computed.
F. Porikli. Constant time O (1) bilateral filtering.In CVPR ’08: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, volume 1, pp. 1 – 8, 2008. This paper shows that with box filters in the spatial domain, the bilateral filter can be computed on the region histogram. As a result, by combining integral histograms, bilateral filtering can be efficiently applied.
Kass, Michael and Justin Solomon. Smoothed Local Histogram Filters.ACM Transactions on Graphics, 29(4): Article No. 100, 2010. This paper uses kerneldensity estimate other than histogram to estimate the local distribution. This paper shows that the computation of kernelsbased estimate for all pixels is equivalent to the convolution of kernel value at all point. As a result, the computation can be done in the frequency domain via FFT, which is independent to the region size.
Markus Hadwiger, Ronell Sicat, Johanna Beyer, Jens Krüger, and Torsten Möller. Sparse PDF maps for nonlinear multiresolution image operations.ACM Transaction on Graphics, 31(6): Article No. 133, 2012. This paper present sparse PDF map. Essentially, sparse PDF map stores the models of span distribution.
Papers in Visualization avenue L. Xu, T.Y. Lee, and H.W. Shen, An InformationTheoretic Framework for Flow Visualization.IEEE Transactions on Visualization and Computer Graphics, 16(6):12161224, Nov.Dec., 2010 The main focus of this paper is to use information theory to guide the visualization of vector fields. In order to apply information theory, local distribution should be efficiently computed in order to compute information theoretic metrics such as entropy, mutual information, and conditional entropy.
D. Thompson, J. Levine, J. Bennett, P.T. Bremer, A. Gyulassy, V. Pascucci, and P. Pebay. Analysis of largescale scalar data using hixels.In LDAV ’11: Proceedings of the IEEE Symposium on Large Data Analysis and Visualization, pp. 23 –30, 2011. Essentially, HIXEL means that each pixel or grid points stores 1 histogram. Based on hixels, this paper show multiple application in scientific visualizaition.
S. Liu, J. Levine, P.T. Bremer, and V. Pascucci. Gaussian mixture model based volume visualization.In LDAV ’12: Proceedings of the IEEE Symposium on Large Data Analysis and Visualization, pp. 73  77, 2012. This paper can be treated as an extension of HIXEL, but the distribution of each pixel is represented by Gaussian mixture models. As storing the parameters of Gaussian kernels is more storageefficient than storing histograms, this paper presents a GPUbased implementation to utilize the distributions for volume visualization.
A. Chaudhuri, T.Y. Lee, B. Zhou, C. Wang, T. Xu, H.W. Shen, T. Peterka and Y.J. Chiang, Scalable Computation of Distributions from Large Scale Data Sets.In LDAV ‘12: IEEE Symposium on LargeScale Data Analysis and Visualization, pp. 113  120, 2012. This paper studies different strategies to parallelize the computation of region histograms on parallel computers.
Steven Martin and HanWei Shen, Transformations for Volumetric Range Distribution Queries.In PacificVis '13: Proceedings of IEEE Pacific Visualization Symposium, pp. 89  96, 2013. This paper presents span distributions, which are the distributions of a spatial interval (or called span). With span distributions, the region histogram of arbitrary axisregion can be computed in logarithmic time.
Zhicheng Liu, Biye Jiang, Jeffrey Heer imMens: RealTime Interactive Visual Exploration of Big DataComputer Graphics Forum, 32(3): pp. 421430, 2013. T.Y. Lee and H.W. Shen Efficient Local Statistical Analysis via Integral Histograms with Discrete Wavelet Transform.IEEE Transactions on Visualization and Computer Graphics, 19(12):26932701, Dec., 2013. As integral histograms provide fast compression, its storage overhead can be large to 3D data. This paper use Wavelet transform to compress the integral histograms to sparse set of coefficients.
Lauro Lins, James T. Klosowski, and Carlos Scheidegger Nanocubes for RealTime Exploration of Spatiotemporal DatasetsIEEE Transactions on Visualization and Computer Graphics, 19(12):2456  2465, 2013. Appendix: Papers for fast bilateral filtering (without explicit computation of histogram computation)Sylvain Paris and Frédo Durand. 2009. A Fast Approximation of the Bilateral Filter Using a Signal Processing Approach.International Journal on Computer Vision, 81(1):2452, 2009. This paper presents an interesting idea to converts the nonlinear bilateral filtering operator to a linear convolution operator into a homogeneous coordinates in the product of the Cartesian grids and the value range.
YANG, Q., TAN, K. H., AND AHUJA, N. Realtime O(1) bilateral filtering.In CVPR ’09: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 557–564, 2009. Sylvain Paris, Pierre Kornprobst, Jack Tumblin and Fr ́edo Durand Bilateral Filtering: Theory and ApplicationsFoundations and Trends in Computer Graphics and Vision, 4(1): 1  7, 2008. 
posted Oct 30, 2013, 9:21 AM by TengYok Lee
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updated Oct 30, 2013, 9:22 PM
]
This is cool. I want to use it to analyze whether certain topics have more self citations than others.
The following paragraph is quoted from the official site http://www.icir.org/christian/scholar.htmlGoogle Scholar is great resource, but it's lacking an
API. Until there is one,
scholar.py
is a Python module that implements a querier and parser for
Google Scholar's output. Its classes can be used
independently, but it can also be invoked as a commandline
tool. It could definitely use a few more features, such as
detailed author extraction and multipage crawling. If
you're interested in adding features, do send patches!
(Thanks to those of you who have—you know who you are.)
Note:This script only use BeautifulSoup. Only version 3 can work. Change the line from BeautifulSoup import BeautifulSoup to from bs4 import BeautifulSoup does not help. Here is the result I search. It seems that only the author name can be used to query, but I prefer to search with the paper title. Otherwise, it will be more helpful if it can return the authors too. $ python scholar.py author="TengYok Lee" "Visualization"
Title An informationtheoretic framework for flow visualization
URL http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5613461
Citations 37
Versions 12
Citations list http://scholar.google.com/scholar?cites=9106169218819595071&as_sdt=2005&sciodt=1,5&hl=en
Versions list http://scholar.google.com/scholar?cluster=9106169218819595071&hl=en&as_sdt=1,5
Year 2010
Title Visualization and exploration of temporal trend relationships in multivariate timevarying data
URL http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5290749
Citations 15
Versions 12
Citations list http://scholar.google.com/scholar?cites=2642186061934580966&as_sdt=2005&sciodt=1,5&hl=en
Versions list http://scholar.google.com/scholar?cluster=2642186061934580966&hl=en&as_sdt=1,5
Year 2009
Title View point evaluation and streamline filtering for flow visualization
URL http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5742376
Citations 14
Versions 7
Citations list http://scholar.google.com/scholar?cites=441434512775469568&as_sdt=2005&sciodt=1,5&hl=en
Versions list http://scholar.google.com/scholar?cluster=441434512775469568&hl=en&as_sdt=1,5
Year 2011
Title Scalable parallel building blocks for custom data analysis
URL http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6092324
Citations 17
Versions 7
Citations list http://scholar.google.com/scholar?cites=13422225121812586831&as_sdt=2005&sciodt=1,5&hl=en
Versions list http://scholar.google.com/scholar?cluster=13422225121812586831&hl=en&as_sdt=1,5
Year 2011
Title A study of parallel particle tracing for steadystate and timevarying flow fields
URL http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6012871
Citations 19
Versions 7
Citations list http://scholar.google.com/scholar?cites=2563345291252687165&as_sdt=2005&sciodt=1,5&hl=en
Versions list http://scholar.google.com/scholar?cluster=2563345291252687165&hl=en&as_sdt=1,5
Year 2011
Title Visualizing timevarying features with tacbased distance fields
URL http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4906831
Citations 8
Versions 8
Citations list http://scholar.google.com/scholar?cites=6779999163248662192&as_sdt=2005&sciodt=1,5&hl=en
Versions list http://scholar.google.com/scholar?cluster=6779999163248662192&hl=en&as_sdt=1,5
Year 2009
Title An imagebased modeling approach to gpubased unstructured grid volume rendering
URL http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.79.3377&rep=rep1&type=pdf
Citations 9
Versions 5
Citations list http://scholar.google.com/scholar?cites=16150139904233454381&as_sdt=2005&sciodt=1,5&hl=en
Versions list http://scholar.google.com/scholar?cluster=16150139904233454381&hl=en&as_sdt=1,5
Year 2006
Title Loadbalanced parallel streamline generation on large scale vector fields
URL http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6064941
Citations 10
Versions 8
Citations list http://scholar.google.com/scholar?cites=15819879486830507892&as_sdt=2005&sciodt=1,5&hl=en
Versions list http://scholar.google.com/scholar?cluster=15819879486830507892&hl=en&as_sdt=1,5
Year 2011
Title Cyclestack: Inferring periodic behavior via temporal sequence visualization in ultrasound video
URL http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5429602
Citations 2
Versions 9
Citations list http://scholar.google.com/scholar?cites=405296597063639844&as_sdt=2005&sciodt=1,5&hl=en
Versions list http://scholar.google.com/scholar?cluster=405296597063639844&hl=en&as_sdt=1,5
Year 2010
Title Exploring flow fields using fractal analysis of field lines
URL http://www.cse.ohiostate.edu/~chaudhua/Publications/postersummary1_vis12.pdf
Citations 1
Versions 2
Citations list http://scholar.google.com/scholar?cites=15634671221111833227&as_sdt=2005&sciodt=1,5&hl=en
Versions list http://scholar.google.com/scholar?cluster=15634671221111833227&hl=en&as_sdt=1,5
Year 2011

