COSC 6344 Visualization

Basic info:

TIME:                            Tu/Th 1~2:30PM                    
LOCATION:                 Science Building S 114                                              
INSTRUCTOR:           Guoning Chen                  
OFFICE HOURS:       Tu/Th 2:30pm-3:30pm taking place virtually via MS Teams (Please check your Teams Calendar for the link)
EMAIL:                         gchen22@central.uh.edu or gchen16@uh.edu               
OFFICE PHONE:       713-743-5788                  

TA:  Nguyen Phan (email: nkphan@uh.edu;   TA Office hours: Mo/Wed 2:30pm-3:30pm taking place virtually via MS Teams)

Note that the tentative format of the course for the first four weeks (August 23 - September 24) will be hybrid, that is,  face-to-face lecture on every Tuesday at S 114 during the scheduled meeting time for the course AND remote teaching via Teams on Thursday. The face-to-face lecturing will be broadcast via Teams for those who cannot join physically. Further changes will be announced depending on the update of the COVID-19 situations.

• Face Covering Policy:To reduce the spread of COVID-19, the University strongly encourages everyone (vaccinated or not) to wear face coverings indoors on campus including classrooms for both faculty and students.    

• Presence in Class: Your presence in class each session means that you:
  

Syllabus (pdf)

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, course webpage, and course team on MS Teams.

• 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%.

• Addendum: Whenever possible, and in accordance with 504/ADA guidelines, the University of Houston will attempt to provide reasonable academic accommodations to students who are registered and approved through the Center for Students with Disabilities. Students are responsible for communicating with the faculty to be sure appropriate arrangements are made.  Registering with CSD is not sufficient.  Communication is critical.  Please call 713-743-5400 for more assistance.

• Academic Honesty:  It is each student’s responsibility to read and understand the Academic Honesty Policy found at http://catalog.uh.edu/content.php?catoid=6&navoid=1025.  There will be no tolerance towards academic dishonesty, and cheating can lead to report of a violation of the Academic Honesty policy to the UH Office of Undergraduate Academic Affairs.  Specifically for this course, 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

•  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  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. Under these policies, students with excused absences will be provided with an opportunity to make up any quiz, exam or other work that contributes to the course grade or a satisfactory alternative. Please read the full policy for details regarding reasons for excused absences, the approval process, and extended absences. Additional policies address absences related to  military service,  religious holy days,  pregnancy and related conditions, and  disability.

NOTE:   The materials provided by the instructor in this course are for the use of the students enrolled in the course only. Copyrighted course materials may not be further disseminated without instructor permission.  This includes sharing content to commercial course material suppliers such as Course Hero or Chegg. Students are also prohibited from sharing materials derived from the instructor’s content (e.g., a student’s lecture notes).

Lecture Slides

Timeline	Lectures	Additional Reading Materials and Resources
Week 1 (08/24, 26)	Course introduction, visualization introduction [slides] Visualization pipeline [slides], different data types and data storage [slides]	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 (08/31, 09/02)	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) [Check the Assignments tab]	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/ A short manual of installing Anaconda and the relevant libraries (including VTK and PyQt) for this course. It also contains some useful libraries to use with Jupyter notebook for scientific data visualization.
Week 3 (09/07, 09)	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 (09/14, 16)	(Due to hurricane Nicholas, the lecture on Tuesday 09/14 was canceled) 2D scalar field visualization - color plots [slides]   (Assignment 2 out) [Check the Assignments tab]	Chapter 1 of the "Visualization Handbook" Chapter 6 of "Data Visualization: Principles and Practice" by Alexandru C. Telea, 2nd Ed.
Week 5 (09/21, 23)	2D scalar field visualization - iso-contours [slides] 3D scalar field visualization - iso-surfacing[slides] Final project introduction (make your choice earlier) [see the assignment tab]	Chapter 6.3 of the "Introduction to Scientific Visualization" Chapters 7, 8, and 9 of the "Visualization Handbook"
Week 6 (09/28, 30)	3D scalar field visualization - DVR - Raycasting [slides] (Assignment 3 out) [Check the Assignments tab] DVR - Splatting and texture-based [slides] Transfer functions - principles and practices [slides]	"Real-Time Volume Graphics", by Klaus Engel, Markus Hadwiger, Joe Kniss, Christof Rezk-Salama, and Daniel Weiskopf. 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 (10/05, 07)	2D vector field visualization - direct method and geometric-based method [slides] 2D vector field visualization - texture-based methods [slides]     (Assignment 4 out) [Check the Assignments tab] Final project proposal due (10/11)	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 (10/12, 14)	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 (10/19, 21)	3D vector field visualization - challenges and common techniques [slides] Unsteady vector field visualization - theory and practice [slides] (Assignment 5 out) [Check the Assignments tab]	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 (10/26, 28)	IEEE Visualization 2021 Conference (Attendance for active students is FREE !!) Self study and review on 10/26! Mid-term exam (in person) on 10/28 (open book, open notes, bring your laptop!)
Week 11 (11/02, 04)	Mid-term exam solution explanation 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 (11/09, 11)	Tensor field visualization - glyphs [slides] Tensor field visualization - geometric based, texture based [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 (11/16, 18)	Graph visualization - Part I [slides] Graph visualization - Part II [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 (11/23, 25)	Higher-dimensional data visualization -- overview [slides] Thanksgiving Holiday!	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 (11/30, 12/02)	Final project presentation

Assignments

Assignment 1: Effective plots and graphs [description] [data]  [Due on 09/09/2021]
Assignment 2: 2D scalar field visualization [description] [data] [ vtk python skeleton code ]  [Due on 09/23/2021]
    [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 on 10/08/2021]
Assignment 4: 2D vector field visualization [description] [data] [vtk_python_skeleton_code] [Due on 10/22/2021]
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 on 11/10/2021]

Final Projects

        If you already have a research topic with a faculty of the department and there is a clearly defined visualization problem in your research, please talk to me to see whether it will make a qualified final project for the course. Otherwise, please choose from the following options.

       Each final project can be carried out by up to three persons. The more members in a project, the more challenging and the larger the project should be. Please talk to me to see whether your choice is suitable for a group project.

The structure of the presentation  (6 mins + 2 mins Q&A, a stopwatch will be used!!!): The final project presentation will take place on Nov. 30 and Dec. 2 in the class. Your presentation should contain: - 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 5! For the final report, please write it in the IEEE TVCG style (5-8 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

       The following is a list of real-world data science problems that I strongly encourage you to consider as your final project options if you do not have a problem from your reserach. Students who choose from the following options will receive a bonus of 5% for their final projects. [The list will be regularly updated with new problems suggested by the faculty]. Also, each problem should not be worked by more than two teams (first-come-first-serve).

• Visual analysis of a traffic light data (suitable for single-person or two-person teams)

• Visualization of a flow diagram built from programs preprocessing big data

• Visualize computation of a machine learning (ML) model evolving with parallel processing

• Effective visualization of big OLAP Cubes

• Effective visualization of contours under different settings

• Past IEEE SciVis contest (till 2018)

      The following is a list of advanced visualization techniques that you can re-implement as your final projects.

• 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

• 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

• 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 .

• 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 ,