COSC6344 Visualization Fall 2020

Syllabus (pdf)

Course summary and description:

Visualization has been established as a powerful means to help domain experts from various disciplines or general audience to MAKE SENSE and PRESENT their data, for decision making. Techniques and knowledge from different sub-fields of computer science (including computer graphics, image processing, data structures and algorithms, high performance computing, machine learning, and human-computer interaction), mathematics, cognitive and perception science, and specific application domains are often adapted for various visualization problems. This introductory course covers topics from a number of sub-fields of visualization and aims to show students how data visualization can help find solutions to a wide range of practical data interpretation problems arising in many areas. Through this course, students are expected to (1) get familiar with important concepts, principles, and techniques/methods for the visualization of different types of data, and (2) foster the ability to select the proper visualization techniques when given a practical data visualization problem. This course serves as one of the core introductory level graduate courses, and it helps build a complete course catalog in the direction of visual computing with courses like image processing, computer graphics, and computer vision.

Prerequisites:

You are expected to have basics knowledge on linear algebra, linear systems, calculus, geometry, numerical analysis, and programming languages. Homework assignments and course projects will require knowledge and experience of C++ and/or Python. Visualization Toolkit (VTK) will be used with either C++ or Python to complete the programming assignments. You need to have solid grasp of data structure and algorithm design. Minimal familiarity with computer graphics principles and techniques is assumed. Having taken COSC 6372: Computer Graphics is ideal but not required.

Textbooks: (recommended, but not required)

Visualization techniques are highly application dependent and highly diversified! There is currently no a good texxtbook that can summarize all available techniques. However, the following textbooks provide a good introduction to some well-established techniques for a number of fundamental visualization problems.

Data Visualization: Principles and Practice. Second Edition. Alexandru C. Telea, A.K. Peters, 2014.
Introduction to Information Visualization. Riccardo Mazza, Springer, 2009.
Charles D. Hansen and Chris R. Johnson, Visualization Handbook, Elsevier, 2004.
Storytelling with Data. Cole Nussbaumer Knaflic, Wiley, 2015.

Grading:

Assignments – 30%
Mid-term exam – 20%
In-class quizzes – 15%
Final project – 30%
In-class participation (including discussion and presentation of papers and projects) - (5%)

A student needs to score on average at least 60% in total to pass the class.
Grading scale (tentative): A: >92%; A-: >88%; B+: >84%; B: >80%; B-: >74%; C+: >68%; C: > 60%.

Excused Absence Policy:

Regular class attendance, participation, and engagement in coursework are important contributors to student success. Absences may be excused as provided in the University of Houston Undergraduate Excused Absence Policy and Graduate Excused Absence Policy for reasons including: medical illness of student or close relative, death of a close family member, legal or government proceeding that a student is obligated to attend, recognized professional and educational activities where the student is presenting, and University-sponsored activity or athletic competition. Additional policies address absences related to military service, religious holy days, pregnancy and related conditions, and disability.

Recording of Class:

Students may not record all or part of class, livestream all or part of class, or make/distribute screen captures, without advanced written consent of the instructor. If you have or think you may have a disability such that you need to record class-related activities, please contact the Center for Students with DisABILITIES. If you have an accommodation to record class-related activities, those recordings may not be shared with any other student, whether in this course or not, or with any other person or on any other platform. Classes may be recorded by the instructor. Students may use instructor’s recordings for their own studying and notetaking. Instructor’s recordings are not authorized to be shared with anyone without the prior written approval of the instructor. Failure to comply with requirements regarding recordings will result in a disciplinary referral to the Dean of Students Office and may result in disciplinary action.

Late Submission Policy:

Late assignments will be marked off 20% for each weekday that it is late. Submissions made 5 days after deadline will not accepted unless due to causes out of control of the students.

Academic Dishonesty:

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.

Honor Code Statement

Students may be asked to sign an honor code statement as part of their submission of any graded work including but not limited to projects, quizzes, and exams: “I understand and agree to abide by the provisions in the University of Houston Graduate Academic Honesty Policy. I understand that academic honesty is taken very seriously and, in the cases of violations, penalties may include suspension or expulsion from the University of Houston."

Syllabus Changes:

Due to the changing nature of the COVID-19 pandemic, please note that the instructor may need to make modifications to the course syllabus and may do so at any time. Notice of such changes will be announced as quickly as possible through UH email, Blackboard, course webpage, and course Piazza.

Students with Disabilities:

Students with documented disabilities who may need accommodations, who have any emergency medical information the instructor should be aware of, or who need special arrangements in the event of evacuation, should make an appointment with the instructor as early as possible, and no later than the first week of the semester. Class materials will be made available in an accessible format upon request.

Resources for Online Learning:

The University of Houston is committed to student success, and provides information to optimize the online learning experience through our Power-On website. Please visit this website for a comprehensive set of resources, tools, and tips including: obtaining access to the internet, AccessUH, and Blackboard; requesting a laptop through the Laptop Loaner Program; using your smartphone as a webcam; and downloading Microsoft Office 365 at no cost. For questions or assistance contact UHOnline@uh.edu.

UH Email:

Email communications related to this course will be sent to your Exchange email account which each University of Houston student receives. The Exchange mail server can be accessed via Outlook, which provides a single location for organizing and managing day-to-day information, from email and calendars to contacts and task lists. Exchange email accounts can be accessed by logging into Office 365 with your Cougarnet credentials or through Acccess UH. They can also be configured on IOS and Android mobile devices. Additional assistance can be found at the Get Help page.

Webcams

Access to a webcam is required for students participating remotely in this course. Webcams must be turned on during lectures and the mid-term exam to allow the monitoring of attendance and to ensure the academic integrity of exam administration.

Please read the following carefully if you need any help

Counseling and Psychological Services (CAPS) can help students who are having difficulties managing stress, adjusting to the demands of a professional program, or feeling sad and hopeless. You can reach CAPS (www.uh.edu/caps) by calling 713-743-5454 during and after business hours for routine appointments or if you or someone you know is in crisis. No appointment is necessary for the “Let's Talk” program, a drop-in consultation service at convenient locations and hours around campus. http://www.uh.edu/caps/outreach/lets_talk.html

Helpful Information

COVID-19 Updates: https://uh.edu/covid-19/
Coogs Care: https://www.uh.edu/dsaes/coogscare/
Laptop Checkout Requests: https://www.uh.edu/infotech/about/planning/off-campus/index.php#do-you-need-a-laptop
Health FAQs: https://uh.edu/covid-19/faq/health-wellness-prevention-faqs/
Student Health Center: https://uh.edu/class/english/lcc/current-students/student-health-center/index.php

Lecture Slides

Timeline	Lectures	Additional Reading Materials and Resources
Week 1 (08/25, 27)	Course introduction, visualization introduction [slides] Visualization pipeline [slides], different data types and data storage [slides] (08/27 lectures were pre-recorded which can be found on Teams)	Chapter 1 of the "Visualization Handbook". Chapters 1 and 2 of "Visualization in Medicine" by Bernhard Preim and Dirk Bartz. Xin Feng et al. "Compact Combinatorial Maps: a Volume Mesh Data Structure," and Alumbaugh and Jiao, "Compact Array-Based Mesh Data Structures," for those who are interested in volumetric mesh representation
Week 2 (09/01, 03)	Cognition and perception, what need to be considered [slides] (Please watch the recording about Gestalt principles on Teams) Elementary plots-principles and practices [slides] (Please watch the recordings for the introduction on some simple plots and a short tutorial for plotting using Python+matplotlib/seaborn on Teams) (Assignment 1 out)	Christopher G. Healey, Perception in Visualization Dulclerci Sternadt Alexandre and Joao Manuel R. S. Tavares, Introduction of Human Perception in Visualization Ware, C. (2013). Information visualization: Perception for design. Chapter 3-5 Jerzy Wieczorek, Data Visualization Principles for Scientific Communication Frank E Harrell Jr., Principles of Graph Construction Marcin Kozak, Basic Principles of Graphing Data Do's and Don'ts for Effective Graphs Instruction on installing Anaconda for coding with Python https://docs.anaconda.com/anaconda/install/ My suggestion of installing Anaconda and the relevant libraries for this course
Week 3 (09/08, 10)	Colors in visualization [slides] VTK introduction [slides] (A simple vtk demo program can be found here. You can run it in Spyder or via command line in the powershell of your conda. You can use the data sets for Assignment 2 for this demo.)	VTK resources: https://vtk.org/ https://lorensen.github.io/VTKExamples/site/Python/ http://www.vtk.org/Wiki/VTK
Week 4 (09/15, 17)	2D scalar field visualization - color plots [slides] 2D scalar field visualization - iso-contours [slides] (Assignment 2 out) Final project introduction (make your choice earlier) [see assignment tab]	Chapter 1 of the "Visualization Handbook" Chapter 6 of "Data Visualization: Principles and Practice" by Alexandru C. Telea, 2nd Ed.
Week 5 (09/22, 24)	3D scalar field visualization - iso-surfacing [slides] 3D scalar field visualization - DVR - Raycasting [slides] (Assignment 3 out)	Chapter 6.3 of the "Introduction to Scientific Visualization" Chapters 7, 8, and 9 of the "Visualization Handbook" "Real-Time Volume Graphics", by Klaus Engel, Markus Hadwiger, Joe Kniss, Christof Rezk-Salama, and Daniel Weiskopf.
Week 6 (09/29, 01)	3D scalar field visualization - DVR - Splatting and texture-based [slides] Transfer functions - principles and practices [slides] Final project proposal due (10/01)	https://lorensen.github.io/VTKExamples/site/VTKBook/07Chapter7/ Gordon Kindlmann. "Transfer functions in direct volume rendering: design, interface, interaction". A siggraph 2002 course note. P Ljung, J Kruger, E Groller, M Hadwiger, CD Hansen, A Ynnerman, State of the art in transfer functions for direct volume rendering, Computer Graphics Forum 35(3), 669-691, 2016
Week 7 (10/06, 08)	2D vector field visualization - direct method and geometric-based method [slides] 2D vector field visualization - texture-based methods [slides] (Assignment 4 out)	Helwig Hauser, Robert S. Laramee, Helmut Doleisch, Frits H. Post, and Benjamin Vrolijk, The State of the Art in Flow Visualization: Direct, Texture-based, and Geometric Techniques, TR-VRVis-2002-046 Technical Report, VRVis Research Center, Vienna, Austria, December 2002. 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, Munich, Germany. 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, 19(9): 1476-1487, 2013
Week 8 (10/13, 15)	2D vector field visualization - Feature-based (phyiscal features) [slides] 2D vector field visualization - Feature-based (topological features) [slides]	Frits H. Post, Benjamin Vrolijk, Helwig Hauser, Robert S. Laramee, and Helmut Doleisch, The State of the Art in Flow Visualisation: Feature Extraction and Tracking, in Computer Graphics Forum (CGF), Vol. 22, No. 4, 2003, pages 775-792. Robert S. Laramee, Helwig Hauser, Lingxiao Zhao, and Frits H. Post, Topology-Based Flow Visualization, The State of the Art, in Topology-Based Methods in Visualization (Proceedings of Topo-In-Vis 2005, 29–30 September 2005, Budmerice, Slovakia), Mathematics and Visualization, H. Hauser, H. Hagen, and H. Theisel editors, pages 1-19, 2007, Springer-Verlag.
Week 9 (10/20, 22)	3D vector field visualization - integral surfaces, texture-based [slides] Unsteady vector field visualization - theory and practice [slides] (Assignment 5 out)	Matt Edmunds, Bob Laramee, Guoning Chen, Nelson Max, Eugene Zhang, and Colin Ware. "Surface-Based Flow Visualization" (STAR report), Computers & Graphics, 36 (8): 974-990, 2012. 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 2008.
Week 10 (10/27, 29)	Mid-term exam; IEEE VIS 2020 (FREE for attendees!!! Attend one session of the talks to earn extra credits!)
Week 11 (11/03, 05)	Mid-term exam review Tensor field visualization - overview [slides]	Kratz, A.; Auer, C.; Stommel, M.; Hotz, I., "Visualization and Analysis of Second-Order Tensors: Moving Beyond the Symmetric Positive-Definite Case", Computer Graphics Forum - State of the Art Reports, 2012. Joachim Weickert, Has Hagen, "Visualization and Processing of Tensor Fields", Google Books, 2007.
Week 12 (11/10, 12)	Tensor field visualization - glyphs [slides] Tensor field visualization - geometric-based, texture-based methods [slides]	Gordon Kindlmann and Carl-Fredrik Westin. "Diffusion tensor visualization with glyph packing," IEEE Visualization 2006. Ropinski, Timo, Steffen Oeltze, and Bernhard Preim. "Survey of glyph-based visualization techniques for spatial multivariate medical data." Computers & Graphics 35, no. 2 (2011): 392-401. Bi, Chongke, Lu Yang, Yulin Duan, and Yun Shi. "A survey on visualization of tensor field." Journal of Visualization 22, no. 3 (2019): 641-660.
Week 13 (11/17, 19)	Graph visualization I [slides] Graph visualization II and tree visualization [slides]	Herman, Ivan, Guy Melançon, and M. Scott Marshall. "Graph visualization and navigation in information visualization: A survey." IEEE Transactions on visualization and computer graphics 6, no. 1 (2000): 24-43. Cui, Weiwei, and Huamin Qu. "A survey on graph visualization." PhD Qualifying Exam (PQE) Report, Computer Science Department, Hong Kong University of Science and Technology, Kowloon, Hong Kong (2007). Gibson, Helen, Joe Faith, and Paul Vickers. "A survey of two-dimensional graph layout techniques for information visualisation." Information visualization 12, no. 3-4 (2013): 324-357.
Week 14 (11/24, 26)	High-dimensional data visualization - introduction [slides] Thanksgiving holiday on Nov. 26 (no class)	Borgo, Rita, Johannes Kehrer, David HS Chung, Eamonn Maguire, Robert S. Laramee, Helwig Hauser, Matthew Ward, and Min Chen. "Glyph-based Visualization: Foundations, Design Guidelines, Techniques and Applications." In Eurographics (STARs), pp. 39-63. 2013. Liu, Shusen, Dan Maljovec, Bei Wang, Peer-Timo Bremer, and Valerio Pascucci. "Visualizing high-dimensional data: Advances in the past decade." IEEE transactions on visualization and computer graphics 23, no. 3 (2016): 1249-1268.
Week 15 (12/01, 03)	Final project presentation (Sign up here)

Assignments

Assignment 1: Effective plots and graphs [description] [data]
Assignment 2: 2D scalar field visualization [description] [data] [vtk python skeleton code]
[vtk_demo, use the data files for assignment 2] [ vtk python skeleton code WITH QT GUI, a short tutorial]
Assignment 3: 3D scalar field visualization [description] [data] [vtk_python_skeleton_code] [sample rendering configuration file] (Due: 10/07/2020!)
Assignment 4: 2D vector field visualization [description] [data] [vtk_python_skeleton_code] (Due 10/23/2020)
Assignment 5: 3D vector field visualization [description] [data] [Use the modified skeleton code for Assignment 4. See details in the description] [arrow3d-Ex.py] (Due 11/11/2020)

Final Projects

The structure of the presentation (8 mins + 2 mins Q&A, a stopwatch will be used!!!): The final project presentation will take place on Dec. 1 and Dec. 3 in the class.

- Problem definition (especially what is the visualization problem you are addressing)

- Describe your technique (mostly on algorithm and visualization/interface design)

- Results and/or demo (Show your current results. Provide necessary interpretation of your visualization. How do you know you have resolved the problem?)

- Future Work (If your results are half-cooked, what else do you still need to do to make it complete before the deadline? If the results are ready/finalized, what do think you can improve further in the future)

Requirements of final project submission

You will need to submit your source code, your final project presentation (.pptx or .pdf), and your report (see below) in a single .zip file via the blackboard system by December 6!

For the final report, please write it in the IEEE TVCG style (4-8 pages including figures and illustrations). You can find the template of this format in the following link (you can find the downloadable templates, words or Latex, on the webpage):

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

The final report should include the following components:

Abstract
Introduction
Related Work
Methodology and Implementation
Results and Discussions
Conclusion and Future Work
List of references

Final project topics

The final project is an individual project. No group project is allowed. Please select from ONLY the following provided topics!

A tree-map visualization for hierarchical data

- Cong Feng, Minglun Gong, Oliver Deussen, and Hui Huang, Treemapping via Balanced Partitioning

Effective graph layout and visualization algorithms

- J. F. Kruiger, P. E. Rauber, R. M. Martins, A. Kerren, S. Kobourov, A. C. Telea, Graph Layouts by t-SNE

- Yunhai Wang, Yanyan Wang, Yingqi Sun, Lifeng Zhu, Kecheng Lu, Chi-Wing Fu, Michael Sedlmair, Oliver Deussen, Baoquan Chen, Revisiting stress majorization as a unified framework for interactive constrained graph visualization

- Danny Holten, Jarke J Van Wijk, Force-directed edge bundling for graph visualization

Effective 3D line rendering

- Sebastian Eichelbaum, Mario Hlawitschka, and Gerik Scheuermann, LineAO—Improved Three-Dimensional Line Rendering

- Tobias Günther, Christian Rössl, Holger Theisel, Opacity optimization for 3D line fields

Volume rendering with light illumination

- Frida Hernell, Patric Ljung, and Anders Ynnerman. Local Ambient Occlusion in Direct Volume Rendering,

- Daniel Jönsson, Erik Sundén, Anders Ynnerman, and Timo Ropinski. A Survey of Volumetric Illumination Techniques for Interactive Volume Rendering

Compute and visualize 2D vector field topology

- Guoning Chen, Konstantin Mischaikow, Robert S. Laramee, Pawel Pilarczyk, and Eugene Zhang. Vector Field Editing and Periodic Orbit Extraction Using Morse Decomposition

Stream surface construction and placement

- Matt Edmunds, Robert S. Laramee, R. Malki, I.Masters, T.N. Croft, Guoning Chen, and Eugene Zhang. Automatic Stream Surface Seeding: A Feature Centered Approach

Illustrative rendering of integral surfaces

- Mathias Hummel, Christoph Garth, Bernd Hamann, Hans Hagen, and Kenneth I. Joy. Iris: Illustrative rendering for integral surfaces

- Tobias Günther, Maik Schulze, Janick Martinez Esturo, Christian Rössl, Holger Theisel. Opacity Optimization for Surface

Texture-based visualization for surface vector fields

- Jin Huang, Zherong Pan, Guoning Chen, Wei Chen, and Hujun Bao. Image-Space Texture-Based Output-Coherent Surface Flow Visualization

- JJ van Wijk, Image based flow visualization for curved surfaces

Texture-based visualization for 2D tensor fields

- Xiaoqiang Zheng and Alex Pang. HyperLIC,

- Eugene Zhang, James Hays, and Greg Turk. Interactive Tensor Field Design and Visualization on Surfaces

Glyph packing for tensor field visualization

- Gordon Kindlmann and Carl-Fredrik Westin. Diffusion tensor visualization with glyph packing

Visualize 3D Cross/frame fields

- Xifeng Gao, Wenzel Jakob, Marco Tarini, Daniele Panozzo. Robust Hex-Dominant Mesh Generation using Field-Guided Polyhedral Agglomeration.

Unsteady flow visualization

- Samer Barakat, Christoph Garth, and Xavier Tricoche. Interactive computation and rendering of finite-time Lyapunov exponent fields

- Kai Buerger, Florian Ferstl, Holger Theisel, and Rüdiger Westermann. Interactive streak surface visualization on the GPU

Ensemble Data visualization

- Potter, Kristin, Andrew Wilson, Peer-Timo Bremer, Dean Williams, Charles Doutriaux, Valerio Pascucci, and Chris R. Johnson. Ensemble-vis: A framework for the statistical visualization of ensemble data

- Marc G Genton, Christopher Johnson, Kristin Potter, Georgiy Stenchikov, Ying Sun. Surface Boxplots.

- Mahsa Mirzargar, Ross T. Whitaker, and Robert M. Kirby. Curve boxplot: Generalization of boxplot for ensembles of curves.

Virtual reality (or augmented reality) techniques for visualization

- Usher, Will, Pavol Klacansky, Frederick Federer, Peer-Timo Bremer, Aaron Knoll, Jeff Yarch, Alessandra Angelucci, and Valerio Pascucci. A virtual reality visualization tool for neuron tracing

- Cordeil, Maxime, Andrew Cunningham, Benjamin Bach, Christophe Hurter, Bruce H. Thomas, Kim Marriott, and Tim Dwyer. IATK: An Immersive Analytics Toolkit, , source code