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ResearchBlog
Interesting SIGGRAPH 2015 papers
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
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. 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) 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 Feature Extraction for DW-MRI Visualization: The State of the Art and Beyond Thomas Schultz TrackVis trackvis.org |
Related tutorials/papers about convergence issue of spectral clustering
I am applying spectral clustering for graph partitioning, but ARPACK can fail to converge to compute the eigenvectors of the smallest 20 eigenvalues. Then I found the following tutorials/papers about the convergence issue: One important thing I learn is that from the tutorial on spectral cluster:On the convergence of spectral clustering on random samples: the normalized case. von Luxburg, U., Bousquet, O., and Belkin, M. In Proceedings of the 17th Annual Conference on Learning Theory (COLT) (pp. 457 – 471). 2004. Limits of spectral clustering. von Luxburg, U., Bousquet, O., and Belkin, M. Advances in Neural Information Processing Systems (NIPS) 17 (pp. 857 – 864), 2005. A Tutorial on Spectral Clustering Ulrike von Luxburg 2007 arXiv:0711.0189 We have to make sure that the eigenvalues of L corresponding to the eigenvectors used in unnormalized spectral clustering are significantly smaller than the minimal degree in the graph. |
Textbooks for scientific visualization
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Interesting visualization projects
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 networkOpening the black box - data driven visualization of neural networksF.-Y. Tzeng and Kwan-Liu Ma Proceedings of IEEE Visualization 2005, pp.383,390, 23-28 Oct. 2005 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 Understanding Deep Image Representations by Inverting Them Aravindh Mahendran, and Andrea Vedaldi |
Several GraphCut papers to read
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
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Recent work on image-based approaches for scientific visualization
Explorable images for visualizing volume data. pdfAnna 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. 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
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
REF: http://ieeevis.org/year/2014/info/overview-amp-topics/accepted-papers SciVisCharacterizing Molecular Interactions in Chemical SystemsDavid 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 TVCG Interpolation-Based Pathline Tracing in Particle-Based Flow Visualization Jennifer Chandler, Harald Obermaier, Ken Joy |
My notes on Logistic Regression
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