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.

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 --> Apr. 17, 2015~~
		~~ACM MM '15 short paper~~		~~Apr. 30, 2015~~
	~~PG '15~~			~~May 8/May 15, 2015~~
	~~SIGGRAPH Asia '15~~			~~Jun. 2, 2015~~
			~~LDAV '15~~	~~Jun. 24, 2015~~
~~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~~

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Interesting SIGGRAPH 2015 papers

posted Jul 8, 2015, 6:22 AM by Teng-Yok Lee

I haven't followed SIGGRAPH papers for a while. I am surprising to see that there are still sections about video processing, time-lapsed videos, and light fields, which are the topics I always want to study more. It should be a good timing to follow up. I will check papers in these sections first.

REF: http://kesen.realtimerendering.com/sig2015.html

Literatures of geometric-based DTI visualization

posted Jun 28, 2015, 10:47 AM by Teng-Yok Lee [ updated Jul 8, 2015, 6:18 AM ]

Visualizing diffusion tensor MR images using streamtubes and streamsurfaces
Song Zhang; Demiralp, C.; Laidlaw, D.H.,
IEEE Transactions on Visualization and Computer Graphics. 9(4): pp.454,462, Oct.-Dec. 2003.
pdf

Exploring Connectivity of the Brain’s White Matter with Dynamic Queries.
Anthony Sherbondy, David Akers, Rachel Mackenzie, Robert Dougherty, and Brian Wandell

In IEEE Vis 2004
page

DTI Fiber Clustering in the Whole Brain
Song Zhang and David Leidlow
In IEEE Vis 2004 (poster session)

pdf

Fast and reproducible fiber bundle selection in DTI visualization
Blaas, J.; Botha, C.P.; Peters, B.; Vos, F.M.; Post, F.H.
In IEEE Vis 2005

pdf

Evaluation of fiber clustering methods for diffusion tensor imaging
Moberts, B.; Vilanova, A.; van Wijk, J.J.,
In IEEE Vis 2005.

pdf

Grid-based spectral fiber clustering.
Jan Klein ; Philip Bittihn ; Peter Ledochowitsch ; Horst K. Hahn ; Olaf Konrad ; Jan Rexilius ; Heinz-Otto Peitgen;
Proc. SPIE 6509, Medical Imaging 2007: Visualization and Image-Guided Procedures, 65091E, 2007.

A Comparison of the Perceptual Benefits of Linear Perspective and Physically-Based Illumination for Display of Dense 3D Streamtubes.
Chris Weigle and David C. Banks,
In IEEE Vis 2008.

A Novel Interface for Interactive Exploration of DTI Fibers

Chen et al.
IEEE Transactions on Visualization and Computer Graphics. 15(6): pp 1433 - 1440, 2009.

Global Illumination of White Matter Fibers from DT-MRI Data
David C. Banks and Carl-Fredrik Westin
pdf

Feature Extraction for DW-MRI Visualization: The State of the Art and Beyond
Thomas Schultz

pdf

TrackVis
trackvis.org

Textbooks for scientific visualization

posted May 2, 2015, 4:10 PM by Teng-Yok Lee [ updated May 13, 2015, 8:41 PM ]

Authors/Book	Note
Visualization in Medicine: Theory, Algorithms, and Applications Bernhard Preim, Dirk Bartz (Google)
The Visualization Handbook Charles D. Hansen, Chris R. Johnson Academic Press, 2005 (Google)
Introduction to Scientific Visualization Helen Wright Springer Science & Business Media, 2007 (Google)
Real Time Volume Graphics K. Engel, M. Hadwiger, J. Kniss, C. Rezk, Salama, and D. Weiskopf A.K. Peters, 2006 (Google)	This book is based on the author's tutorials for SIGGRAPH.
Isosurfaces: Geometry, Topology, and Algorithms Rephael Wenger CRC Press, Jun 24, 2013 (Google)	The author's publication page provides a sample chapter.
Flow visualization, 2nd Edition. Wolgang Merzkirch Academic Press, 1987 (Google, Elseiver)
Scientific Visualization: Techniques and Applications K.W. Brodlie, L.A. Carpenter, R.A. Earnshaw, J.R. Gallop, R.J. Hubbold, A.M. Mumford, C.D. Osland, P. Quarendon Springer Science & Business Media, 1992 (Google, Springer)	This book is based on a set of documents written for the AGOCG Workshop in UK, 1991.
An Introductory Guide to Scientific Visualization Rae Earnshaw, Norman Wiseman. Springer Science & Business Media, 1992 (Google, Springer)

Interesting visualization projects

posted Sep 30, 2014, 5:05 PM by Teng-Yok Lee [ updated Jun 9, 2015, 4:49 PM ]

Real neural network

http://nxxcxx.github.io/Neural-Network/

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/

Artificial neural network

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

Visualization of Artificial Neural Network with WebGL
MARKUS SPRUNCK
http://www.sw-engineering-candies.com/blog-1/experimental-visualization-of-artificial-neural-network-with-webgl

Visualizing and Understanding Convolutional Networks
Matthew D. Zeiler and Rob Fergus
In ECCV 2014

pdf

Understanding Deep Image Representations by Inverting Them
Aravindh Mahendran, and Andrea Vedaldi

In CVPR 2015.

pdf

Misc

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

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

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

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): 1610-1627 (2013)
Yuri Boykov and Vladimir Kolmogorov	An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision	IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(9):1124-1137 (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): 1222-1239 (2001)
Vladimir Kolmogorov	Convergent Tree-reweighted Message Passing for Energy Minimization	IEEE Transactions on Pattern Analysis and Machine Intelligence 28(10): 1568-1583 (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): 147-159 (2004)
Nikos Komodakis, Georgios Tziritas, Nikos Paragios	Performance vs computational efficiency for optimizing single and dynamic MRFs: Setting the state of the art with primal-dual strategies	Computer Vision and Image Understanding 112(1), 14-29 (2008)
Nikos Komodakis, Nikos Paragios, Georgios Tziritas	MRF Energy Minimization and Beyond via Dual Decomposition	IEEE Transactions on Pattern Analysis Machine Intelligence 33(3): 531-552 (2011)
Ben Taskar, Carlos Guestrin and Daphne Koller	Max-Margin 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, 6:1453-1484 (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): 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.

pdf

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.

pdf

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.

pdf

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.

pdf

[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

pdf

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

preprint, video

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. h_t(x) =1/(1+exp(-t^Tx)) 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/dt_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^Tx⁽ⁱ⁾))^-1
= -(1 + exp(-t^Tx⁽ⁱ⁾))^-2 exp(-t^Tx⁽ⁱ⁾) x⁽ⁱ⁾_j

= -{1/(1 + exp(-t^Tx⁽ⁱ⁾)} {exp(-t^Tx⁽ⁱ⁾) / (1 + exp(-t^Tx⁽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.

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