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IT Uppsala universitet
Scientific Visualization5 hp
Fall 2010
Filip Malmberg [email protected]
Ch 1. IntroductionCh 2. Computer Graphics primerCh 4. The Visualization pipeline
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Filip Malmberg ([email protected]) Stefan Seipel ([email protected]) Patrik Malm ([email protected]) Pontus Olsson ([email protected]) Gustaf Kylberg ([email protected])
Teachers
Centre for Image Analysis, CBAITC building 2, floor 1Office 2111
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About me PhD-student in image processing. Working with medical volume images. Using visualization as a tool in my research.
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About the course 10 lectures 3 Computer exercises/assignments (3 hp) Written exam (2 hp)
Textbook:The Visualization Toolkit, An Object-Oriented Approach to 3D Graphics,4th edition, Kitware, Inc., 2006ISBN 1-930934-19-X
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Course webpagehttp://www.it.uu.se/edu/course/homepage/vetvis/ht10/
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What is visualization?Dictionary:vi·su·al·ize
• To form a mental image of; envisage: try to visualize the scene as it is described
• To make visible
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Interpreting data in visual terms When data is complex: Collected/Computed
When numerous data Visualization is not a substitute to,
but in addition to, statistical analysis and other quantitative methods
Visualization takes advantage of human sensory abilities• Pattern recognition, Trend discovery, etc.
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Example: Graphs
0500
1000150020002500300035004000
Mfl
op/s
ec
2 4 8 16 32 64
Number of Processors
158,7601,121,320
Unknowns
IBM SP2
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Some more sophisticated examples
Nuclear, Quantum, and Molecular Modeling
Structures, Fluids, and Fields
Advanced Imaging and Data Management
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Scientific visualization Scientific visualization is the process of
exploring, transforming, and viewing data as images
The dimensionality of the data is generally larger than or equal to 3
Visualization is often interactive We are not trying to create realistic images,
but to visualize data in an informative way Dependent on the task given
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Visualization serves many purposes ”Pretty pictures” For further analysis Debugging ...
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Visualization can be used in every step of most processes
Problem formulation Mathematical modelling Software/Hardware Simulation Result Interpretation
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General development of visualization
Rather new discipline still developing into sub-areas
Multi-disciplinary (Computer science, human perception, computer graphics, HCI)
Faster computers, high-speed networks, new user-interfaces
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Ch 3: Computer graphics primer Creating images with a computer – 3D
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Digital images
Image of a rat Close-up of rat nose
94 100 104 119 125 136 143 153 157 158103 104 106 98 103 119 141 155 159 160109 136 136 123 95 78 117 149 155 160110 130 144 149 129 78 97 151 161 158109 137 178 167 119 78 101 185 188 161100 143 167 134 87 85 134 216 209 172104 123 166 161 155 160 205 229 218 181125 131 172 179 180 208 238 237 228 200131 148 172 175 188 228 239 238 228 206161 169 162 163 193 228 230 237 220 199
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Color
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Visible spectrum Light=electromagnetic radiation
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Colour The eye’s and the brain’s impression of electromagnetic
radiation in the visual spectra
How is colour percieved?
Lightsource
Reflecting object
Detector
rods & cones
red-sensitive
green-sensitive
blue-sensitive
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Human color perception
3 types of “detectors”->We can think of colour as a “3D space”.
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RGB/ CMY colour space
RGB - for additive colour mixing, e.g., on a computer screenCMY - for subtractive colour mixing, e.g., in printing or painting
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HLS colour space Hue: dominant wavelength, tone Lightness: Intensity, brightness Saturation, dilution by white
Important aspects:•Intensity decoupled from colour•Related to how humans perceive colour
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Computer graphicsComponents: Model: geometry, surface properties, … Lighting: number, positions, properties Viewpoint Projection
Computer graphics is the foundation of data visualization... but visualization is more than computer graphics!
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Rendering Generation of an image from data. “Physical” view:
• Rays of light are emitted from a light source• Some rays strike an object• The object surface absorbs some light and reflects
the rest• Some reflected light enter our eyes (or a camera)• We ”see” the object
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Ray-tracing or ray-casting Ray-tracing simulates the interaction of light
with objects by following the path of each light ray.
In computations: • follow the rays backwards from the eye into the
scene
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Computationally: Ray casting Only rays that reach the eye matter Reverse direction and cast rays, instead of
trace rays Need at least one ray per pixel
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Image-order and object-order methods
Ray-tracing is an image-order process, determining what happens to each pixel (ray of light), one at a time
An object-order process works by rendering each object, one at a time
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Rasterization Line drawing Polygon (triangle) filling
Scan one line at a time:scan conversion/ scan line rendering
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What's so interesting about triangles?
Easy to handle mathematically (e.g. always flat)
In CG, complex surfaces are often approximated by a large number of triangles.
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// Image order renderingfor all pixels for all objects calculate the object's contribution to the pixel colour endend
Image-order and object-order methods
// Object order renderingfor all objects for all pixels (occupied by the object!) calculate the object's contribution to the pixel colour endend
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Lights in CG We simplify by assuming (infinitely distant)
point-shaped light sources Light is emitted in all directions from a single
point in space Far away implies that rays are parallel
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Light-surface interaction The Phong reflection model =
• Ambient reflection +• Diffuse reflection +• Specular reflection
n
l
rv
φθ
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Phong reflection model
Illustration of the Phong Reflection Model. Created by Brad Smith, August 7, 2006.
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Polygonal shading
Flat Gouraud Phong
Illumination depends on normal vector!
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Local vs. Global illumination
Historically:
Ray tracing
Scan line
Global illumination
Local illumination
Offline rendering
Realtime rendering
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Surface vs. volume rendering For simplicity, we can view the surface of
objects and their interactions with light However, translucent objects scatter light and
we need to consider properties inside the objects
Chapter 7 describes volume rendering (Lecture 4)
vs.
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Camera model
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Camera movementsMovement around focal point Movement around camera center
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Coordinate systems4 coordinate systems: Model: where the object is defined World: 3D space where actors are positioned View: what is visible to the camera Display: (x, y) pixel locations See Figure 3-14
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Coordinate transformations 3D to 2D Perspective Rotation, translation, scaling Homogeneous coordinates 4x4 transformation matrices
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Hidden surface removal Inter-object occlusion is an important depth
cue. Needs to be handled correctly when rendering
3D objects.
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Z-buffer algorithm
fill z-buffer with infinite distancefor all objects for each (object) pixel calculate object's z-value if z(x,y) is closer than z-buffer(x,y) draw pixel z-buffer(x,y)=z(x,y) end endend
Note: With ray tracing, we get this for “free”
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Learning more about CG• Computer graphics, 10 hp• Period 3
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Ch 4. The visualization pipeline Computer graphics: Convert graphics
primitives to images Visualization: Convert data to graphics
primitives
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Visualization pipelineThe pipeline consists of objects to represent data objects to operate on data indicated direction of data flow (arrow
connections between objects)
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Data objects Represent information Provide methods to create, access, and delete
data To modify data is not really allowed; reserved
for process objects
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Process objects Operate on input data to generate output data New data or new form
Source objects • initiate (read, generate) visualization data flow
Filter objects • maintain visualization data flow
Mapper objects • terminate (write, graph) visualization data flow
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A visualization pipeline
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Pipeline topology How to connect data objects and process
objects Pipeline connections
• type concerns the form of data that process objects take as input or generate as output
• multiplicity deals with # of input and # of output allowed
Feedback loops• view intermediate results
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Executing the pipeline Causing each process object to operate Most often repeated executions due to user
interaction• change parameters of process object• change input to process object
For efficiency reasons, see to that only execute the process objects whose input has changed
Synchronization between process objects required prior to execution
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Memory and computation trade-off Visualization is resource demanding in
• computer memory due to input size• computational times due to algorithm complexity
Static memory model• intermediate data saved to reduce overall
computation Dynamic memory model
• intermediate data discarded, but may have to be re-computed
Combination of static and dynamic models
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Next lecture Intro to VTK and Python. More about the visualization pipeline, as
implemented in VTK.
IT Uppsala universitet
Scientific Visualization5 hp
Fall 2010
Filip Malmberg [email protected]
Ch 1. IntroductionCh 2. Computer Graphics primerCh 4. The Visualization pipeline
