ResearchBlog

News from participating projects

Here 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.


 VisGraphics
Computer Vision / Image processing
 HPCSubmission 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

Interesting visualization projects

posted Sep 30, 2014, 5:05 PM by Teng-Yok Lee   [ updated Oct 10, 2014, 2:23 PM ]

For neural network.
http://nxxcxx.github.io/Neural-Network/

Inviso: Visualization Hadoop performance by Netflix.
https://github.com/Netflix/inviso

VisNEST - Visualization of Simulated Neural Brain Activity
http://www.jara.org/en/research/jara-hpc/research/details/csg-immersive-vis/visnest-visualization-of-simulated-neural-brain-activity/

Visualizing MNIST: An Exploration of Dimensionality Reduction
https://colah.github.io/posts/2014-10-Visualizing-MNIST/

Opening the black box - data driven visualization of neural networks
F.-Y. Tzeng and Kwan-Liu Ma
Proceedings of IEEE Visualization 2005, pp.383,390, 23-28 Oct. 2005
pdf




Several GraphCut papers to read

posted Sep 30, 2014, 8:29 AM by Teng-Yok Lee   [ updated Sep 30, 2014, 8:29 AM ]

These papers are the suggestion to-read list of the Coursera course Discrete Inference and Learning in Artificial Vision (instructors: Nikos Paragios and Pawan Kumar)

https://www.coursera.org/course/artificialvision

AuthorsTitleBook
Chaohui Wang, Nikos Komodakis, Nikos ParagiosMarkov Random Field modeling, inference & learning in computer vision & image understanding: A surveyComputer Vision and Image Understanding 117(11): 1610-1627 (2013)
Yuri Boykov and Vladimir KolmogorovAn Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in VisionIEEE Transactions on Pattern Analysis and Machine Intelligence, 26(9):1124-1137 (2004)
Yuri Boykov and Olga VekslerGraph Cuts in Vision and Graphics: Theories and ApplicationsHandbook of Mathematical Models in Computer Vision, edited by Nikos Paragios, Yunmei Chen and Olivier Faugeras. Springer, 2006
Yuri Boykov, Olga Veksler and Ramin ZabihFast Approximate Energy Minimization via Graph CutsIEEE Transactions on Pattern Analysis and Machine Inteligence, 23(11): 1222-1239 (2001)
Vladimir KolmogorovConvergent Tree-reweighted Message Passing for Energy MinimizationIEEE Transactions on Pattern Analysis and Machine Intelligence 28(10): 1568-1583 (2006)
Vladimir Kolmogorov and Ramin ZabihWhat Energy Functions can be Minimized via Graph Cuts?IEEE Transactions on Pattern Analysis and Machine Inteligence, 26(2): 147-159 (2004)
Nikos Komodakis, Georgios Tziritas, Nikos ParagiosPerformance vs computational efficiency for optimizing single and dynamic MRFs: Setting the state of the art with primal-dual strategiesComputer Vision and Image Understanding 112(1), 14-29 (2008)
Nikos Komodakis, Nikos Paragios, Georgios TziritasMRF Energy Minimization and Beyond via Dual DecompositionIEEE Transactions on Pattern Analysis Machine Intelligence 33(3): 531-552 (2011)
Ben Taskar, Carlos Guestrin and Daphne KollerMax-Margin Markov NetworksProceedings of Advances in Neural Information Processing Systems: (2003)
Ioannis Tsochantaridis, Thorsten Joachims, Thomas Hofmann and Yasmine AltunLarge Margin Methods for Structured and Interdependent Output VariablesJournal of Machine Learning Research, 6:1453-1484 (2005)
Tomas WernerRevisiting the Linear Programming Relaxation Approach to Gibbs Energy Minimization and Weighted Constraint SatisfactionIEEE Transactions on Pattern Analysis and Machine Intelligence 32(8): 1474-1488 (2010)


Recent work on image-based approaches for scientific visualization

posted Sep 29, 2014, 9:42 PM by Teng-Yok Lee   [ updated Oct 3, 2014, 7:05 PM ]

Explorable images for visualizing volume data.
Anna Tikhonova, Carlos D. Correa, Kwan-Liu Ma.
In Proceedings of IEEE Pacific Visualization Symposium 2010, pp. 177-184, 2010.

An Exploratory Technique for Coherent Visualization of Time-varying Volume Data.
Anna Tikhonova, Carlos D. Correa, Kwan-Liu Ma.
Computer Graphics Forum, 29(3):783-792, 2010.
pdf

Visualization by Proxy: A Novel Framework for Deferred Interaction with Volume Data.

Anna Tikhonova, Carlos D. Correa, Kwan-Liu Ma.
IEEE Transactions on Visualization and Computer Graphics, 16(6):1551-1559, 2010.

An Image-based 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:

Visibility-Driven Transfer Functions.
Carlos D. Correa, Kwan-Liu Ma.
In Proceedings of IEEE Pacific Visualization Symposium 2009, 2009.

Visibility Histograms and Visibility-Driven Transfer Functions.
Carlos D. Correa, Kwan-Liu Ma.
IEEE Transactions on Visualization and Computer Graphics, 17(2):192-204, 2011.

[FW] Common Misconceptions about Data Analysis and Statistics

posted Sep 27, 2014, 10:12 AM by Teng-Yok Lee

An article to refresh my memory on statistics?

Motulsky HJ, Common Misconceptions about Data Analysis and Statistics. J Pharmacol Exp Ther. 351(1):200-5, 2014 Oct.
doi: 10.1124/jpet.114.219170.
http://jpet.aspetjournals.org/content/351/1/200.full.pdf

IEEE Vis '14 papers to read

posted Sep 7, 2014, 11:07 PM by Teng-Yok Lee   [ updated Sep 24, 2014, 5:25 PM ]

REF: http://ieeevis.org/year/2014/info/overview-amp-topics/accepted-papers

SciVis

Characterizing Molecular Interactions in Chemical Systems
David Guenther, Roberto Alvarez Boto, Julia Contreras Garcia, Jean-Philip Piquemal, Julien Tierny

Vortex Cores of Inertial Particles
Tobias Günther, Holger Theisel

Advection-Based Sparse Data Management for Visualizing Unsteady Flow
Hanqi 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

Fixed-Rate Compressed Floating-Point Arrays
Peter Lindstrom

Sparse PDF Volumes for Consistent Multi-Resolution Volume Rendering
Ronell Sicat, Jens Krueger, Torsten Möller, Markus Hadwiger
preprint

TVCG
Interpolation-Based Pathline Tracing in Particle-Based Flow Visualization
Jennifer Chandler, Harald Obermaier, Ken Joy

My notes on Logistic Regression

posted Jun 9, 2014, 10:32 PM by Teng-Yok Lee   [ 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. ht(x) =1/(1+exp(-tTx)) is the logistic function with parameter theta (t).

F(t) = 1/m sumi = 1 ... m y(i) log ht(x(i)) + (1 - y(i))log (1 - ht(x(i)))


The partial gradient w.tr.t tj is denoted as d/dtj = dj. Then the partial gradient at tj, aka, dj F(t) is derived as follows:

dj F(t
= 1/m sumi = 1 ... m y(i) / ht(x(i)) dj ht(x(i)) + (1 - y(i))/(1 - ht(x(i))) dj (1 - ht(x(i)))
= 1/m sumi = 1 ... m y(i) / ht(x(i)) dj ht(x(i)) -  (1 - y(i))/(1 - ht(x(i))) dj ht(x(i)) <-- Remove the constant 1, which is in boldface above.
= 1/m sumi = 1 ... m dj ht(x(i)) {y(i) / ht(x(i))  -  (1 - y(i))/(1 - ht(x(i)))}  <-- Separate dj ht(x(i)) from both terms.
= 1/m sumi = 1 ... m dj ht(x(i)) {y(i) (1 - ht(x(i))) - ht(x(i))(1 - y(i))}/{ht(x(i))(1 - ht(x(i))} 
= 1/m sumi = 1 ... m dj ht(x(i)) (y(i) - ht(x(i)))/{ht(x(i))(1 - ht(x(i))} 


where
dj ht(x(i))
= dj (1 + exp(-tTx(i)))-1
= -(1 + exp(-tTx(i)))-2 exp(-tTx(i)) x(i)j
= -{1/(1 + exp(-tTx(i))} {exp(-tTx(i)) / (1 + exp(-tTx(i))} x(i)j
= -{ht(x(i))(1 - ht(x(i))} x(i)j


Thus

dj F(t)
= 1/m sumi = 1 ... m dj ht(x(i)) (y(i) - ht(x(i)))/{ht(x(i))(1 - ht(x(i))}
= 1/m sumi = 1 ... m (y(i) - ht(x(i))) {-{ht(x(i))(1 - ht(x(i))} x(i)j}/{ht(x(i))(1 - ht(x(i))}
= 1/m sumi = 1 ... m (ht(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] = ht(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 ht(x) + (1- y) log 1 - ht(x)
That's why optimizing the cost function F is equivalent to find the Maximum Likelihood Estimator.

Visualization of MPI/Parallel Programs

posted Jun 4, 2014, 10:50 PM by Teng-Yok Lee

Research articles


Visualizing Large-scale Parallel Communication Traces Using a Particle Animation Technique
Carmen Sigovan, Chris Muelder, and Kwan-Liu Ma
In Proceedings of EuroVis 2013.
pdf

Gantt Chart Visualization for MPI and Apache Multi-Dimensional Trace Files
Wu, C.E. and Bolmarcich, A.
In IPDPS 2002.
pdf

Software packages




Video Visualization

posted Dec 20, 2013, 10:03 PM by Teng-Yok Lee   [ updated Feb 27, 2014, 6:41 PM ]

A Survey on Video-based Graphics and Video Visualization
Rita Borgo, Min Chen, Ben Daubney, Edward Grundy, Gunther Heidemann, Benjamin Höferlin, Markus Höferlin, Heike Jänicke, Daniel Weiskopf and Xianghua Xie.
http://www.vis.uni-stuttgart.de/~weiskopf/publications/eg11_star.pdf

Video Visualization
http://www.oerc.ox.ac.uk/projects/video-visualization

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 time-lapse video.
Eric P. Bennett and Leonard McMillan.
In SIGGRAPH 2007, Article 102, 2007.

Factored time-lapse video.
Kalyan Sunkavalli, Wojciech Matusik, Hanspeter Pfister, and Szymon Rusinkiewicz.
In SIGGRAPH 2007, Article 101, 2007.

Exploring video streams using slit-tear 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.

Action-based 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 Kwan-Liu Ma.
In SIGGRAPH '10, Article 88 , 9 pages, 2010.

Computation and Application of Histograms in Image Processing, Graphics and Visualization

posted Nov 9, 2013, 10:30 PM by Teng-Yok Lee   [ 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.

  1. The papers in computer graphics and image processing are more related to faster filtering for images/videos.
  2. 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).
  3. 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.
  4. On the other hand, scientific visualization conferences also have papers about fast histogram query. These papers are mainly from the research group of Prof. Han-Wei 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. 519-526, 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 kernel-density estimate other than histogram to estimate the local distribution. This paper shows that the computation of kernels-based 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 non-linear multi-resolution 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 Information-Theoretic Framework for Flow Visualization.
IEEE Transactions on Visualization and Computer Graphics, 16(6):1216-1224, 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 large-scale 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 storage-efficient than storing histograms, this paper presents a GPU-based 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 Large-Scale 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 Han-Wei 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 axis-region can be computed in logarithmic time.

Zhicheng Liu, Biye Jiang, Jeffrey Heer
imMens: Real-Time Interactive Visual Exploration of Big Data
Computer Graphics Forum, 32(3): pp. 421-430, 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):2693-2701, 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 Real-Time Exploration of Spatiotemporal Datasets
IEEE 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):24-52, 2009.

This paper presents an interesting idea to converts the non-linear 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.
Real-time 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 Applications
Foundations and Trends in Computer Graphics and Vision, 4(1): 1 - 7, 2008.

FW: scholar.py - A parser for Google Scholar, written in Python

posted Oct 30, 2013, 9:21 AM by Teng-Yok Lee   [ 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.html
Google 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 command-line tool. It could definitely use a few more features, such as detailed author extraction and multi-page 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="Teng-Yok Lee" "Visualization"
         Title An information-theoretic 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 time-varying 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 steady-state and time-varying 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 time-varying features with tac-based 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 image-based modeling approach to gpu-based 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 Load-balanced 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.ohio-state.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


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