COSC 6344 Visualization (Fall 2015)



Instructor: Guoning Chen
Office: PGH 566
Email: chengu at cs . uh . edu
Lecture time: Tue/Thur 10~11:30AM
Location: AH 302

Office Hours: Tue/Thur 11:30AM~12:30PM or by appointment



Syllabus (pdf version)

Visualization has been established as a powerful means to help data owners from almost every discipline to make sense and present their diverse data, in order to verify or falsify hypotheses and discover new knowledge. The logic of visualization is to take advantages of our interpretation capability via visual perception channel and to encode information of the data into images and plots to improve our understanding of the data. Techniques from computer science, mathematics, cognitive and perception science, and physics are often adapted for various visualization problems. This introductory course covers topics from a number of sub-fields of visualization including information visualization and scientific visualization, and aims to show students how data visualization can help find solutions to a wide range of practical data interpretation problems occurring in many areas. Through this course, the students are expected to get familiar with a number of important techniques and methods for addressing the visualization of different basic types of data, and foster the ability to determine the proper visualization techniques when given a practical problem. This course serves as one of the core introductory level graduate courses, and helps build a complete course catalog in visual computing direction with the existing courses on imaging and computer graphics.
You are expected to know basics of linear algebra, linear systems, calculus, geometry, numerical analysis, and programming languages. Homework assignments and course projects will require knowledge of C/C++ and OpenGL library. Minimal familiarity with computer graphics principles and techniques is assumed. Having taken COSC-6372: Computer Graphics is encouraged but not required.

Project assignments – 50%;

Mid-term exam – 15%;

Final project – 25%;

In-class participation (including discussion and presentation of papers) 10%

A student needs to score on average at least 60% in total to pass the class.

Grading criterion: A(>=90); A-(>=85); B+(>=80); B (>=75); B- (>=70)

Late assignments will be marked off 10% for each weekday that it is late. Each student can ask for the extension of one assignment up to 5 weekdays.

Please do your own work. The default consequence for academic dishonesty is a failure for the course. It is okay to discuss with other students general ideas about implementing a program. It is NOT okay to copy another student's program. It is okay to discuss possible program bugs. It is NOT okay to debug another student's program.



Tentative Schedule

TIMELINE
MATERIAL COVERED
WEEK 1 (08/25, 27) Class Preliminaries
Introduction – History of visualization, Visualization pipeline, Data types and representations, PLY format
WEEK 2 (09/01, 03) OpenGL tutorial, Skeleton code; Elementary plots (Assignment 1 out)
WEEK 3 (09/08, 10) Colors in visualization, Color coding and iso-contouring for 2D scalar field visualization (Assignment 2 out)
WEEK 4 (09/14, 17) Iso-contouring (Assignment 3 out); Iso-surfacing and cutting planes
WEEK 5 (09/22, 24) Direct volume rendering – Ray casting and Splatting, texture-based (Assignment 4 out)
WEEK 6 (09/29, 10/01) Transfer function design; Flow visualization introduction; Flow visualization techniques in 2D (arrow and color plots and streamlines);
WEEK 7 (10/06, 08) Flow visualization techniques in 2D - texture-based (Assignment 5 out) ; Non-Topology-based vector field visualization
WEEK 8 (10/13, 15) Topology-based vector field visualization; Final project topic review
WEEK 9 (10/20, 22) 3D flow visualization; Mid-term review
WEEK 10 (10/27, 29) Mid-term exam; Visualization system IEEE Visualization 2015
WEEK 11 (11/03, 05) Unsteady flow visualization (Assignment 6 out); Tensor data application – introduction and math
WEEK 12 (11/10, 12) Direct methods (Glyphs); Geometric-based and texture-based tensor visualization
WEEK 13 (11/17, 19) Information visualization – graph and hierarchy data visualization
WEEK 14 (11/24) Information visualization – high dimensional data visualization; visual analytics (Thanksgiving)
WEEK 15 (12/01, 03) Final project presentations (see the requirement below)
WEEK 16 (12/08) Final report due on December 11 via blackboard system



Lectures and Reading Materials

1. Introduction, visualization pipeline, data types

What is visualization? What does it do? Why is it important? We will have answers for these questions in this week. Also, it is important to tell the difference of scientific data and information data. Can you? Next, we will learn the basic pipeline of visualization (based on my personal opinion and experience). Can you identify the most important step from this pipeline? What are the general types of data and how do people characterize them? What are their representations? Do you know how to parse a given data file?

Lec1_slides

Lec2_slides

Additional materials:

2. OpenGL tutorial

For most of your project assignments, you will need to work on OpenGL library using C/C++. This lecture provides you a short tutorial of OpenGL. Do not expect to fully master it in one or two lectures. You should expect some learning curve. The best way to get yourself familar with this library is to try and fail. There are many examples out there on the Internet. Try to play with them by changing things here and there. In addition, I provide the skeleton code for all the project assignments. Please start playing with it now (not later)!!!

Lec3(OpenGL_tutorial)_slides (pdf, pptx)

Additional materials:

Many other resources can be found on the Internet, just Google "OpenGL"

3. Plots

Everyone is using plots to some extent in their tasks (e.g., writing papers and reports, making presentations) now and then. How well do your plots convey the information that they are suppose to? In this lecture, we will learn a number of important principles on what you should or should not do when making some basic (or elementary) plots.

Lec4_slides

Additional materials:

4. Colors in visualization

How important colors are in visualization? We will try to answer this question in this week and learn some basic rules for using colors in the visualization. In addition, while colors are essential, they are typically one attributes of other geometry primitives that the rendering are concerned with. What are these geometry primitives, how should we use them for various visualization tasks? We will learn these in this lecture.

Lec5_slides

Additional materials:

5. Scalar field visualization I - color plots and iso-contouring/iso-surfacing

Scalar fields widely exist in many applications. Its visualization thus plays an important role in these various areas. We have briefly shown how to use color to convey the scalar values in Lecture 3. Another important visualization technique for scalar fields is to visualize the points with the same specific scalar value as one or more geometry. In 2D, this geometry can be represented as a set of curves, while in 3D they are some surfaces. The specific value is typically selected by the user during the data exploration and visualization interaction.

Lec6_slides (color plots and iso-contouring)

Lec6_part2 (iso-surfacing)

Additional materials:

6. Scalar field visualization II - direct volume rendering

Visualizing 3D volumetric scalar fields as a whole without losing information is sometime necessary. The technique to do so is a natural extension of the color plots for 2D fields. This technique does not require to create intermediate geometric elements. We, thus, call it "direct volume rendering". There are a number of computational strategies for DVR, including image-order (e.g. raycasting), object-order (e.g. splatting), and domain-dependent (e.g. shear-warp). For all these methods, the two most important steps are the specification of transfer functions and the composition of colors and opacity.

Lec7_slides (ray casting)

Lec8_slides (splatting)

Lec8_2 slides (texture-based)

Additional materials:

7. Scalar field visualization II - transfer function

Designing the proper transfer functions for various data visualization problems is an essential practice. As we have briefly touched the usage of proper transfer functions in getting meaningful volume rendering result, in this lecture we provide a little bit more insights into the definitions of the transfer functions given a volumetric scalar data set.

Lec9_slides

Additional materials are provided within the individual slides!

8. 2D Vector field (or flow) visualization

Vector fields are a common form of data that are generated from various sources. They are widely used to study the behaviors of gas and liquids under different circumstances, which is dominated by certain aero- and hydro- dynamical systems. Visualizing and analyzing vector fields is important to the understanding of these different dynamical systems. In this part of the lecture, we will learn the basic concepts of vector fields and some simple but effective visualization techniques including the direct method and the geometric-based (or integral curves) method.

Lec10_slides (introduction and streamlines)

Additional reading:

9. Texture-based flow visualization

Texture-based method is currently a very popular technique for the visualization of 2D and 2.5D (surface) flows with the advantages of full space coverage and hardware acceleration. LIC (line integral convolution) is considered the first successful texture-based method. Many variations have been proposed since then. Most of them adopt the LIC with different implementation improvement. IBFV (image-based flow visualization) is another popular technique that utilizes the hardware ability. We will mostly focus on these two techniques and their variations in this lecture.

Lec11_slides

Additional reading:

10. Feature-based vector field visualization

In many situations, the given vector fields are overly complex and large-scale so that a simply visualization cannot draw the attention of the domain experts to those "features" that they are interested in. In that case, it is better to first identify those features of interest, then highlight them on top of the other visualizations. This leads to the feature-based visualization. There are two types of features in flow: the non-topological features and topological features. The non-topological features are typically defined and computed based on some local physical properties of the flow. On the other hand, vector field topology provides the qualitative (or structural) information of the flow data. It also provides a partitioning scheme for spatial domain segmentation so that the flow has homogeneous behavior within a region (i.e. flowing from one repeller to one attractor). This topic will specifically focus on the extraction of topology for 2D steady vector fields. A number of extension of this standard topology will be briefly reviewed as well.

Lec12_slides (non-topology-based and topology)

Lec12(2)_slides (compute topology)

Additional reading:

11.3D vector field visualization

3D flows have much more complex configuration than their counterpart. In addition, the occlusion of 3D visualization makes the task even more challenging. This topic will review a number of geometric based methods for the effective visualization of 3D flows.

Lec13_slides

Additional reading:

12. Unsteady vector field visualization

Time-dependent flows represent the most popular flow data. The additional time dimension greatly increases the difficulty of their analysis because the originally well-defined features in steady flows are not valid any more under time-varying setting. We will review a number of visualization and analysis techniques that can help partially address the visualization of time-dependent flows.

Lec14_slides

Additional reading:

13. Tensor field background and its applications

Tensor fields are now a popular subject in the visualization community. It measures the local higher ordered property on a higher dimensional manifold compared to scalar (0D property) and vector (1D property). Examples include stress/strain tensor, curvature tensor, metric tensor, diffusion tensor, and velocity gradient tensor. This lecture will emphasize the importance of tensors with a number of applications, and introduce the basic concepts and computations of this complex data form with the focus on second order tensors.

Lec15_slides

Additional reading:

14. Tensor field visualization: glyph packing

Glyphs are good at describing the local patterns of tensors. With proper placement technique, the trend of the placed glyphs can reveal certain global patterns of the tensor field. We will look at a few glyph design and packing schemes in this lecture, especially the superquardic glyphs and the particle-system based glyph packing.

Lec16_slides

Additional reading:

15. Tensor field visualization: texture-based and geometric-based methods

This lecture will cover the texture-based and geometric-based visualization techniques for second order tensor visualization. Two extended texture-based methods will be introduced, including the hyperLIC and extended IBFV. Hyperstreamline computation and placement will be introduced for the geometric-based visualization as well as its application in diffusion tensor imaging.

Lec17_slides

Additional reading:

16. Graph visualization

Graphs are one of those common data representations that we see from many applications. Any relation information (either directional or non-directional) can be represented by a graph with the graph nodes corresponding to the objects and the edges representing the relations between objects. In many real-world examples, the relations between subjects are overwhelmingly complex, typically in higher-dimensional forms. This requires an intuitive visualization to help people understand the patterns and trends encoded in these graphs. In this lecture, we will briefly describe a number of representative and basic graph visualization techniques. Many more advanced techniques can be derived from them.

Lec18_slides

Lec19_slides

Additional reading:

17. Higher dimensional data

Higher dimensional data visualization and analysis is getting more and more attention these days as many data and the relation encoded in them are in high dimension. Due to our limited knowledge in the higher dimensional space beyond our physical world and the constraints of 2D display devices, producing intuitive visualization for these types of data is specifically challenging. We will review some of issues and research problems of this topic, and introduce one of the standard techniques for visualizing high dimensional data, i.e. the parallel coordinate.

Lec20_slides

Additional reading:


Assignments (with sample skeleton code and data)

Skeleton code (VS10, VS12, for Mac (sorry that the skeleton code for Mac OS is out-of-date) )

Assignment 1(elementory plots) (data file)

Assignment 2 (2D scalar field visualization - Part I) (data file)

Assignment 3 (2D scalar field visualization - Part II)

Assignment 4 (3D scalar field visualization)

Assignment 5 (2D vector field visualization - LIC)(data file)

Assingment 6 (3D vector field visualization - streamlines, stream ribbon, animation)


Requirements of final project submission

You will need to submit your source code, your final project presentation (.pptx or .pdf), and your report in a single .zip file via the blackboard system by December 11!

The following describes the requirements to the final project presentation and final report.

For the final project presentation, please prepare a 10-mins talk, followed by a 2-4mins Q&A session. The talk should include the following components:

For the final report, please write it in the IEEE TVCG style (4-8 pages with figures and illustrations). You can find the template of this format in the following link:

http://www.computer.org/web/tvcg/author

The final report should include the following components:

Final project tentative topics:

1. Do you have any urgent visualization problem at hand that are involved in your research? If you do, please come talk to me and see whether it can be used for the course project.

2. GPU-based real-time volume rendering

J. Kruger and R. Westermann, Acceleration Techniques for GPU-based Volume Rendering. IEEE Visualization 2003.

P. Schlegel, M. Makhinya and R. Pajarola. Extinction-Based Shading and Illumination in GPU Volume Ray-Casting. IEEE TVCG Vol. 17(12): pp. 1795 - 1802, 2011.

Daniel Jonsson, Erik Sunden, Anders Ynnerman, and Timo Ropinski. Interactive Volume Rendering with Volumetric Illumination. Eurographics STAR program - 2012.

Open source for your reference: Exposure render.

3. Advanced texture-based flow visualization

IBFVS: Jarke J. van Wijk, Image Based Flow Visualization for Curved Surfaces, IEEE Visualization 2003. [demo program].

ISA: Robert S. Laramee, Bruno Jobard, and Helwig Hauser, Image Space Based Visualization of Unsteady Flow On Surfaces. in Proceedings of IEEE Visualization (IEEE Vis 2003), pages 131-138, October 19-24, 2003, Seattle, Washington.Robert S. Laramee, Jarke J. van Wijk, Bruno Jobard, and Helwig Hauser, ISA and IBFVS: Image Space Based Visualization of Flow on Surfaces in IEEE Transactions on Visualization and Computer Graphics (IEEE TVCG), Vol. 10, No. 6, November/December 2004, pages 637-648.Jin Huang, Zherong Pan, Guoning Chen, Wei Chen, and Hujun Bao. Image-Space Texture-Based Output-Coherent Surface Flow Visualization, IEEE Transactions on Visualization and Computer Graphics, Vol. 19 (9): pp. 1476-1487, 2013.

Variations of LIC on surfaceProfessor Zhanping Liu's webpage on flow visualization is a good place to check.

4. Vector field topology

Entity Connection Graph (ECG)

Guoning Chen, Konstantin Mischaikow, Robert S. Laramee, Pawel Pilarczyk, and Eugene Zhang. "Vector Field Editing and Periodic Orbit Extraction Using Morse Decomposition". IEEE Transactions on Visualization and Computer Graphics, Vol. 13, No. 4, 2007, pp. 769-785.

Morse Connection Graph (MCG)

Guoning Chen, Konstantin Mischaikow, Robert S. Laramee, and Eugene Zhang. "Efficient Morse Decompositions of Vector Fields". IEEE Transactions on Visualization and Computer Graphics, Vol. 14, No. 4, 2008, pp. 848-862.Andrzej Szymczak and Eugene Zhang. Robust Morse Decompositions of Piecewise Constant Vector Fields, IEEE Transactions on Visualization and Computer Graphics, 18(6), 938-951, 2012.

3D vector field topology

H. Theisel, T. Weinkauf, H.-C. Hege, H.-P. Seidel. Saddle connectors-an approach to visualizing the topological skeleton of complex 3D vector fields. IEEE Visualization 2003, pp. 225-232.Other 3D field analysis technique T Weinkauf, H Theisel. Curvature measures of 3D vector fields and their applications. Journal of WSCG 10 (2), 507-514, 2002.

Out-of-core vector field analysis (new research, no much work on this topic yet)

5. Time-varying vector field analysis

Feature tracking

T. Weinkauf, H. Theisel, A. Van Gelder, and A. Pang. Stable Feature Flow Fields. IEEE Transactions on Visualization and Computer Graphics 17(6), June 2011 .

T. Schafhitzel, K. Baysal, M. Vaaraniemi, U. Rist, D. Weiskopf. Visualizing the Evolution and Interaction of Vortices and Shear Layers in Time-Dependent 3D Flow, IEEE Transactions on Visualization and Computer Graphics, Vol. 17, No. 4, 412-425, 2011.

X. Tricoche, T. Wischgoll, G. Scheuermann, and H. Hagen. Topology Tracking for the Visualization of Time-Dependent Two-Dimensional Flows. Computer & Graphics 26, 2002, pp. 249-257.

C. Garth, X. Tricoche, and G. Scheuermann. Tracking of Vector Field Singularities in Unstructured 3D Time-Dependent Data Sets. Proc. IEEE Visualization '04, 2004, pp. 329-336.

FTLE for unsteady flow

Christoph Garth, Florian Gerhardt, Xavier Tricoche, Hans Hagen, "Efficient Computation and Visualization of Coherent Structures in Fluid Flow Applications", in "IEEE Transactions on Visualization and Computer Graphics (Proceedings IEEE Visualization 2007)", Volume 13, Number 6, pp 1464--1471, 2007.

F. Sadlo and D. Weiskopf. Time-Dependent 2-D Vector Field Topology: An Approach Inspired by Lagrangian Coherent Structures. Computer Graphics Forum, Vol. 29, No. 1, 88-100, 2010.

Markus Uffinger, Filip Sadlo, Member, IEEE, and Thomas Ertl. A Time-Dependent Vector Field Topology Based on Streak Surfaces. IEEE TVCG (accepted, prePrint).

6. 3D (time-varying) vector field visualization

Geometric-based method

Christoph Garth, Hari Krishnan, Xavier Tricoche, T. Bobach, Ken Joy, "Generation of Accurate Integral Surfaces in Time-Dependent Vector Fields", in "Proceedings of IEEE Visualization '08", 2008.

Hari Krishnan, Christoph Garth, Ken Joy, "Time and Streak Surfaces for Flow Visualization in Large Time-Varying Data Sets", in "Proceedings of IEEE Visualization '09", pp 1267--1274, 2009.

Matthew Edumunds, Tony McLoughlin, Robert S. Laramee, Guoning Chen, Eugene Zhang, and Nelson Max, Advanced, Automatic Stream Surface Seeding and Filtering, in Theory and Practice of Computer Graphics (TPCG) 2012, pages 53-60, 13-14 September 2012, Didcot, Oxfordshire, UK.

More geometric techniques can be found in a recent survey paper: Tony McLoughlin, Robert S. Laramee, Ronald Peikert, Frits H. Post, and Min Chen, Over Two Decades of Integration-Based, Geometric Flow Visualization in EUROGRAPHICS 2009, State of the Art Reports, pages 73-92, 30 March - 3 April 2009.

Texture-based method

Victoria Interrante and Chester Grosch, "Strategies for Effectively Visualizing 3D Flow with Volume LIC," Proceedings of IEEE Visualization 97. Oct 19-24, Phoenix, Arizona, pp. 421-424, 1997.

C. Rezk-Salama, P. Hastreiter, C. Teitzel, and T. Ertl, "Interactive Exploration of Volume Line Integral Convolution Based on 3D-Texture Mapping," Proceedings of IEEE Visualization 99, Oct 24-29, San Francisco, California, pp. 233-240, 1999.

Zhanping Liu and Robert J. Moorhead II, "A Texture-Based Hardware-Independent Technique for Time-Varying Volume Flow Visualization," Journal of Visualization, Vol. 8, No. 3, pp. 235~244, 2005.

7. Diffusion tensor imaging

Gordon Kindlmann and Carl-Fredrik Westin. Diffusion tensor visualization with glyph packing, IEEE Visualization 2006.

G. Reina, K. Bidmon, F. Enders, P. Hastreiter, and T. Ertl. GPU-Based Hyperstreamlines for Diffusion Tensor Imaging. EuroVis 2006.

An old survey, Diffusion Tensor Imaging: Concepts and Applications. 2001.


8. Asymmtric tensor field visualization

Guoning Chen, Darrel Palke, Zhongzang Lin, Harry Yeh, Paul Vincent, Robert S. Laramee and Eugene Zhang. "Asymmetric Tensor Field Visualization for Surfaces", IEEE TVCG (Proceeding of IEEE Visualization 2011), Vol.17, No. 12, pp 1979-1988, 2011.

Eugene Zhang, Harry Yeh, Zhongzang Lin, and Robert S. Laramee, "Asymmetric Tensor Analysis for Flow Visualization", IEEE Transactions on Visualization and Computer Graphics, Vol. 15(1), 2009, pp. 106-122.

Xiaoqiang Zheng, Alex Pang. 2D asymmetric tensor field analysis, IEEE Visualization 2005.

9. Illustrative visualization

Xuexiang Xie, Ying He, Feng Tian, Hock-Soon Seah, Xianfeng Gu, and Hong Qin. An Effective Illustrative Visualization Framework Based on Photic Extremum Lines (PELs). IEEE Visualization 2007.

Cheng-Kai Chen, Shi Yan, Nelson Max, and Kwan-Liu Ma. An Illustrative Visualization Framework for 3D Vector Fields. Computer Graphics Forum, vol. 30, no. 6, pp. 1941–1951, September 2011.

A. Brambilla, R. Carnecky, R. Peikert, I. Viola and H. Hauser, Illustrative Flow Visualization: State of the Art, Trends and Challenges, Eurographics STAR Reports, pp. 75-94, 2012.

IEEE Visualization Tutorial on Illustrative Visualization


10. Information visualization

Large scale dynamic graph visualization

Michael Burch, Corinna Vehlow, Fabian Beck, Stephan Diehl, and Daniel Weiskopf. Parallel Edge Splatting for Scalable Dynamic Graph Visualization. IEEE Information Visualization 2011.

Danny Holten and Jarke J. van Wijk. Force-Directed Edge Bundling for Graph Visualization. EuroVis2009

Gautam Kumar and Michael Garland. Visual Exploration of Complex Time-Varying Graphs, IEEE Information Visualization 2006.

High-dimensional data visualization

Enrico Bertini, Andrada Tatu, and Daniel Keim, Quality Metrics in High-Dimensional Data Visualization: An Overview and Systematization. IEEE Information visualization 2011.

S. Gerber, P.-T. Bremer, v. Pascucci, and R. Whitaker, Visual Exploration of High Dimensional Scalar Functions. IEEE Transactions on Visualization and Computer Graphics 16(6), pp. 1271-1280, 2010 (this is actually a scientific visualization paper).