CMPUT905
Introduction to Scientific
Visualization
Special Graduate Course
Autumn 2006
The
art of visualization centers around helping people explore or explain data
through software systems that provide static or interactive visual representations.
Visualization designers exploit the high bandwidth channel of human visual
perception to allow people to comprehend information orders of magnitude more
quickly than they could through reading raw numbers or text. Visualization is
useful for detecting patterns, assessing situations, sharing data, and steering
simulation. Understanding, and, ultimately, knowledge cannot be delivered
directly from computation. Visualization is the tool through which computation
presents a face to an end user and, by so doing, is allows the user to derive knowledge from data.
During the past 20 years the world has
experienced an “information big bang”, an exponential explosion of data. New information
produced in the two years since 2003 exceeds the information contained in all
previously created documents. Of all this new information produced since 2003,
more than 90% takes digital form, vastly exceeding information produced in
paper and film forms. But raw information is by itself of questionable value.
We are continually challenged to make sense of the enormous growth and
onslaught of information and use it in effective and efficient ways.
Among
the greatest scientific challenges of the 21st century, will be to effectively
understand and make use of the vast amount of information being produced. Our
primary problem is no longer acquiring sufficient information, but rather
making use of it and sharing it in a collaborative fashion. If we are to use
information to make discoveries in science, engineering, medicine, art, and the
humanities, we must create new theories, techniques, and methods for its
management and analysis. By its very nature, visualization addresses the
challenges created by such excess – too many data points, too many variables,
too many time steps, and too many potential explanations. Thus, as we work to
tame the accelerating information explosion and employ it to advance
scientific, biomedical, and engineering research, visualization will be
among our most important tools. This course aims at introducing to
scientist, engineers, as well as practitioners in medicine the basic
fundamentals of data visualization. During the course, we will focus on the
following topics:
- Visualization Software
Toolkits
- Perception and
Visualization Design
- Segmentation and Surface
Extraction
- Volumetric Methods for
Medical Data
- Flow Visualization
Techniques for CFD
- Information Visualization
Techniques
- Virtual Reality Interfaces
for Visualization
Prerequisites:
Basic knowledge of computer graphics and parallel programming is an asset.
Textbooks
There is no specific textbook for this course. We will provide notes and
scientific papers.
Several downloadable documents from Kitware that describe VTK, Paraview, and
VolView will be used as part of teaching the use of a visualization toolkit.
These include the Paraview
user's guide, and the VolView
user's guide. A description on how to get Paraview, VolView, and VTK can be
found here.
Evaluation
The students will be evaluated based on three criteria:
- Class
Participation: Class participation will count for 10% of the final
mark. This will include presenting papers in class and to work on small
assignments performed during class hours.
- Project:
The project is where the students get really evaluated; it will count for
60% of the total score. It has to be a significant peace of work that
needs to be approved by the instructor. Project scope can range from implementing
algorithms presented in scientific publications or new ones as long as the
scope of the work does not exceed the time limit of the session.
- Project
Report and Presentation: Each student will have to write a final
report on their project in the form of a ten pages paper in IEEE double
columns format. In addition, the student will have to make a 20 minutes
presentation of their work during a special workshop that will be
organized during the term. This will count for 30% of the final mark.
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Project
All
students must complete a term project. Students must work individually. The project consist of the
various aspect of sensor based 3D modeling using photo-grammetry and range
sensors. The results on the
projects are to be demonstrated near the end of the term as part of a
presentation to the class. A final paper is also required. Eac
h student must
complete a term project. Details on the projects can be found on
the web. The results on the projects are required to be presented near the end
of the term. Completion of the project is a requirement for the course. Failure
to submit your project before the last day of classes will result in a failing
grade. In addition, a demo must be given at a scheduled time.
Evaluation
The proportion of the final mark
associated with the different components of the course are as follows:
A graduate student who fails to submit the project on the last
day of this class will receive a failing grade.
Textbook
Multiple
View Geometry in Computer Vision
by Richard Hartley and Andrew Zisserman
Cambridge University Press
Alternative Book:
The Geometry from Multiple Images
by Olivier Faugeras and Quan-Tuan Luong
MIT Press
Note: The lectures
do not follow any textbooks.
Other resources:
Course website http://cs.ualberta.ca/~pierreb/CMPU613/
Good
Tutorial: http://www.cs.unc.edu/~marc/tutorial.pdf
Instructor
Dr. Boulanger graduated
from Laval University in Engineering Physics. He also received his Masters in
Physics from the same university and his Ph.D. in Electrical Engineering from
the University of Montreal. He worked for 18 years at the National
Research Council of Canada as a senior research officer where his primary
research interest were in 3D computer vision, rapid product development, and
virtualized reality systems. Since July 1st 2001, he is working as a
professor at the Department of Computing Science at the University of
Alberta doing research and teaching on virtualized reality systems. He is also
an adjunct scientist and principal investigator for new media at TRLabs and at
the Banff Centre. In 2004, Dr. Boulanger was awarded an iCORE industrial chair
in Collaborative Virtual Environment.
He has published more than 150 scientific papers in various
Journals and Conferences. He is on the editorial board of two major academic
journals. Dr. Boulanger is also on many international committees and frequently
gives lectures on rapid product development and virtualized reality. He is the
founder of the Canadian Virtualized Reality Systems Working Group. He is also
the Director of the Advanced Man
Machine Interface Laboratory. On the commercial side, Dr Boulanger is the president
of PROTEUS Consulting Inc., an Alberta-based consulting firm specialized in
Virtual Reality Applications.
Course Material:
The course material
includes notes posted on the web
and additional assigned reading. A detailed list of topics covered in
assignments will be published as the course progresses.
Related Links
NASA list of
visualization sites. Please consult a Gallery of Data Visualization web
page by with Tuftian-like commentary on the visual presentation of
information.
Visualization Toolkits
The Visualization Toolkit (VTK).
Advanced Visual Systems (AVS) home page.
Also, the International AVS Centre, which
has tutorials and downloadable AVS modules as well as other information.
Bill Hibbard's VisAD
and Vis5D packages.
OpenDX (data explorer) home page.
Other Visualization Courses
A portion of this course was taught at SIGGAPH 2003 by Russ Taylor, Colin
Ware, and Victoria Interrante as course #45, "Perceptually
Based Visualization Design."
The ACM SIGGRAPH Education for Visualization Committee produced a draft
report on a curriculum
for visualization education.
UNC Information and Library Science INLS 110-99, "Selected Topics:
Information Visualization," taught by Rolf Daessler (visiting from
Potsdam, Germany) in the summer of 2001.
Utah CS5630, "Scientific
Visualization," taught by Chris Johnson in the spring of 2002.
University of Maryland Baltimore County CSMC491B/691B, "Visualization Techniques,"
taught by David Ebert and Penny Rheingans in the autumn of 1999.
University of New Hampshire OE/CS 867:767, "Interactive Data Visualization,"
taught by Colin Ware.
Virginia Tech ESM4714, "Scientific Visual
Analysis with Multimedia," taught by Ron Kriz in the spring of 2000.
UC Davis ECS 177, "Introduction
to Visualization," taught by Ken Joy in the winter of 1999.
FSU CIS4930/5930, "Visualization,"
taught by David Banks in 2000.
RIT CSC 472/672, "Data
Visualization," taught by G. Scott Owen in the fall of 1996.
CS 237, "Interdisciplinary
Scientific Visualization," taught by David Laidlaw and Daniel Acevedo
in the fall of 2003 and in prior years by David Laidlaw, is a project-based
course that teams computer scientists with domain scientists to
"fund," design, implement, and evaluate a project in scientific
visualization.
The San Diego State University offered "Scientific Visualization" as CS
689 in the fall of 2000.
Two courses on image processing for volume graphics, transfer functions, and
level sets can be found at the National
Library of Medicine site.
Visualization Groups and Sites
Lawrence Livermore
National Laboratories. Los Alamos
National Laboratories. Lawrence Berkeley
National Laboratories. NASA
Ames.