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Visualization
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COCS 485
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5
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0
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0
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5
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COCS 481
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Course
Objectives:
Visualization is a technique used to transform vast amounts of raw
data into graphic representations that utilize the superior visual processing
capability of the human brain to detect patterns and inferences. The purpose of
this course is to provide students with a fundamental understanding of
visualization and its basic principles. The course will begin by building upon
the knowledge acquired in basic 3D graphics courses, such as transformation
geometry, rendering, shading, and the generation of three-dimensional viewing
systems. These fundamental principles will then be applied to a series of
visualization problems. Additionally, we will explore topics in the fields of
image processing, visual perception, mathematics, and computer science, in
order to derive more effective visualizations in a more efficient manner.
Ultimately, the course will equip students with the skills and knowledge
necessary to create and utilize visualizations to better understand and
communicate complex data.
Course
outcomes:
Upon finishing this course, the student should be able to:
· Effectively address the demands of real-world
problems that require advanced visualization techniques.
· Expand knowledge and skills in area of
visualization and be able to derive more effective solutions and visualize more
complex problems. And the student is expected to apply the knowledge he has
gained from foundational 2D/3D graphics courses to the visualization of scalar
volume data.
· Discuss Grid Structures Scientific data sets
and interpolation methods and provide a comprehensive insight into the diverse
methods and techniques employed in effective visualization of complex data
sets.
· Distinguish variety of techniques for the
visualization of scalar fields and algorithms that use color and/or opacity to
represent scalar values.
· Describe slicing, contour curves, surfaces and
various volume rendering techniques: ray casting, 3D texture-based volume
rendering, cell sorting and projection.
· Compare and contrast variety of techniques for
the visualization of vector fields and algorithms that will include the
approximation and visualization of path, stream, streak, and time lines,
surfaces, and tubes, as well as texture-based and glyph-based methods.
· Understand the distinction between scientific
visualization of continuous quantitative data and information visualization of
discrete or non-quantitative data.
Assessment
Strategy:
Students will be assessed in this course based on a set of projects,
assignments, exams presentations.
Textbook:
· Alexandru C. Telea, Data Visualization: Principles and
Practice.
· Klaus Engel et al, Real-Time
Volume Graphics.
· Brian R. Kent, 3D Scientific Visualization with
Blender.
· Helen Wright, Introduction to Scientific
Visualization.
· Georges Bonneau, Scientific Visualization: The Visual Extraction
from Knowledge from data.
Other
References:
· Extra Resources: books, papers, Internet…etc.
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Time table for distributing theoretical course contents
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Week
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Theoretical course contents
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Remarks
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1
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Introduction to visualization and visual
analytics
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2
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Scientific visualization, Volume Data,
Resampling, Interpolation
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3
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Volume Visualization
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4
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Classification and Transfer Functions
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5
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Isosurfaces
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6
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Volume
Rendering
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7
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Advanced Lighting
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MidExam
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8
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Illustrative
Visualization
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9
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Vector Field
Visualization
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10
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Irregular Grids
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11
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Information
Visualization
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12
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Software
visualization
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Final Exam
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