Discrete Data Structure and Colour Mappinghomepages.inf.ed.ac.uk/tkomura/cav/presentation10.pdf ·...
Transcript of Discrete Data Structure and Colour Mappinghomepages.inf.ed.ac.uk/tkomura/cav/presentation10.pdf ·...
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Visualisation : Lecture 5
Discrete Data Structure and Colour MappingColour Mapping
Computer Animation and Visualisation –Lecture 10
Taku Komura
Taku Komura Colour Mapping 1
Institute for Perception, Action & BehaviourSchool of Informatics
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Visualisation : Lecture 5
Overview
� Discrete data structure
� Topology and interpolation
Topological dimension� Topological dimension
� Data Representation
− Structure � Topology - Cells
� Geometry - points
− Attributes
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− Attributes
� Colour Mapping
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Visualisation : Lecture 5
Discrete Vs. Continuous
� Real World is continuous− eyes designed towards the perception of continuous shape
� Data is discrete� Data is discrete− finite resolution representation of a real world (of abstract) concept
� Difficult to visualise continuous shape from raw discrete sampling
we need topology and interpolation
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− we need topology and interpolation
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Visualisation : Lecture 5
Topology� Topology : relationships within the data invariant
under geometric transformation
− Which vertex is connected with which vertex by an edge?− Which vertex is connected with which vertex by an edge?
− Which area is surrounded by which edges?
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Visualisation : Lecture 5
Interpolation & Topology
� If we introduce topology our visualisation of discrete data improves
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− why ? : because then we can interpolate based on the topology
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Visualisation : Lecture 5
Interpolation & Topology
� Use interpolation to shade whole cube:
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− Interpolation: producing intermediate samples from a discrete representation
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Visualisation : Lecture 5
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Visualisation : Lecture 5
Importance of representation
� What happens if we change the representation?
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� Discrete data samples remain the same− topology has changed ⇒ effects interpolation ⇒ effects visualisation
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Visualisation : Lecture 5
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Visualisation : Lecture 5
Topological Dimension� Topological Dimension: number of independent continuous variables
specifying a position within the topology of the data
− different from geometric dimension (position within general space)
Topological GeometricTopological Geometricpoint 0D 2D/3D e.g. 2D (x,y)
curve 1D 2D/3D e.g. 3D point on curve
surface 2D 3D (in general)
volume 3D 3D e.g. MRI or CT scan
Temporal volume 4D 3D (+ t)
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Visualisation : Lecture 5
Structure of Data
� Structure has 2 main parts
− topology : “set of properties invariant under certain geometric transformations”geometric transformations”
� determines interpolation required for visualisation
� “shape” of data
− geometry
� instantiation of the topology
OR
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� specific position of points in geometric space
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Visualisation : Lecture 5
Data Representation
� Data objects : structure + value
− referred to as datasets
Structure- Topology- Geometry
—————————Cells, points (grid)
—————————
Consists of
Abstract
Concrete
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—————————Data Attributes
—————————Scalars, vectorsNormals, texture
coordinates, tensors etc
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Visualisation : Lecture 5
Dataset Representation of the Structure
� Points specify where the data is known
− specify geometry in ℝN
Cells allow us to interpolate between points
ℝ
� Cells allow us to interpolate between points
− specify topology of pointsPoint with known attribute data (i.e. colour ≈ temperature)
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Cell (i.e. the triangle) over which we can interpolate data.
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Visualisation : Lecture 5
Cells
� Fundamental building blocks of visualisation
− our gateway from discrete to interpolated data
� Various Cell Types
− defined by topological dimension
− specified as an ordered point list (connectivity list)
− primary or composite cells
composite : consists of one or more primary cells
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� composite : consists of one or more primary cells
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Visualisation : Lecture 5
Zero-dimensional cell types� Vertex
− Primary zero-dimensional cell− Definition: single point
� Polyvertex− Composite zero-dimensional cell
� composite : comprises of several vertex cells
− Definition: arbitrarily ordered set of points
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Visualisation : Lecture 5
One-dimensional cell types� Line
− Primary one-dimensional cell type− Definition: 2 points, direction is from first to
second point.second point.
� Polyline− Composite one-dimensional cell type− Definition: an ordered set of n+1 points, where
n is the number of lines in the polyline
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n is the number of lines in the polyline
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Visualisation : Lecture 5
Two-dimensional cell types - 1� Triangle
− Primary 2D cell type
− Definition: counter-clockwise ordering of 3 points� order of the points specifies the direction of the surface normal
� Triangle strip
− Composite 2D cell consisting of a strip of triangles
− Definition: ordered list of n+2 points� n is the number of triangles
� Quadrilateral
− Primary 2D cell type
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− Definition: ordered list of four points lying in a plane� constraints: convex + edges must not intersect� ordered counter-clockwise defining surface normal
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Visualisation : Lecture 5
Two-dimensional cell types - 2� Pixel
− Primary 2D cell, consisting of 4 points� topologically equivalent to a quadrilateral
constraints: perpendicular edges; axis aligned� constraints: perpendicular edges; axis aligned
− numbering is in increasing axis coordinates
� Polygon− Primary 2D cell type− Definition: ordered list of 3 or more points
� counter-clockwise ordering defines surface normal� constraint: may not self-intersect
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� constraint: may not self-intersect
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Visualisation : Lecture 5
Three-dimensional cell types� Tetrahedron
− Definition: list of 4 non-planar points
� Six edges, four faces
� Hexahedron
− Definition: ordered list of 8 points
� six quadrilateral faces, 12 edges, 8 vertices
� constraint: edges and faces must not intersect
� Voxel
− Topologically equivalent to Hexahedron
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− constraint: each face is perpendicular to a coordinate axis
− “3D pixels”
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Visualisation : Lecture 5
Attribute Data� Information associated with data topology
− usually associated to points or cells
� Examples :
− temperature, wind speed, humidity, rain fall (meteorology)
− heat flux, stress, vibration (engineering)
− texture co-ordinates, surface normal, colour (computer graphics)
� Usually categorised into specific types:
− scalar (1D)
− vector (commonly 2D or 3D; ND in ℝN)
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− vector (commonly 2D or 3D; ND in ℝN)
− tensor (N dimensional array)
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Visualisation : Lecture 5
Attribute Data : Scalar
� Single valued data at each location
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− simplest and most common form of visualisation data
volume density (MRI)elevation from reference plane
ozone levels
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Visualisation : Lecture 5
Attribute Data : Vector Data
� Magnitude and direction at each location
− 3D triplet of values (i, j, k)− 3D triplet of values (i, j, k)
Wind Speed
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− Also Normals – vectors of unit length (magnitude = 1)
Magnetic Field
Force / DisplacementWind Speed
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Visualisation : Lecture 5
Attribute Data : Tensor Data
� K-dimensional array at each location
− Generalisation of vectors and matrices− Tensor of rank k can be considered a k-dimensional table− Tensor of rank k can be considered a k-dimensional table
� Rank 0 is a scalar� Rank 1 is a vector� Rank 2 is a matrix� Rank 3 is a regular 3D array
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� Example : Tensor Stress
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Visualisation : Lecture 5
Visualisation Algorithms� Generally, classified by attribute type
− scalar algorithms (e.g. colour mapping)
− vector algorithms (e.g. glyphs)− vector algorithms (e.g. glyphs)
− tensor algorithms (e.g. tensor ellipses)
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Visualisation : Lecture 5
Scalar Algorithms
� Scalar data : single value at each location
� Structure of data set may be 1D, 2D or 3D+� Structure of data set may be 1D, 2D or 3D+
− we want to visualise the scalar within this structure
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� Two fundamental algorithms
− colour mapping (transformation : value → colour)
− contouring (transformation : value transition → contour)
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Visualisation : Lecture 5
Colour Mapping
� Map scalar values to colour range for display− e.g.
� scalar value = height / max elevation� scalar value = height / max elevation
� colour range = blue →red
� Colour Look-up Tables (LUT)
− provide scalar to colour conversion
− scalar values = indices into LUT
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Visualisation : Lecture 5
Colour LUT� Assume
− scalar values Si in range {min→max}
− n unique colours, {colour0... colourn-1} in LUT0 n-1
� Define mapped colour C :
− if Si < min then C = colourmin
− if Si > max then C = colourmax
− else
colour0
colour1colour2
SiC
LUT
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− else
colourn-1
C= colour�S
i− min
max− min�
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Visualisation : Lecture 5
Colour Transfer Function� More general form of colour LUT
� A continuous function instead of a discrete LUT
− scalar value S; colour value C− scalar value S; colour value C
− colour transfer function : f(S) = C
− e.g. define f() to convert densities to realistic skin/bone/tissue colours
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skin/bone/tissue colours
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Visualisation : Lecture 5
Colour Spaces - RGB� Colours represented as R,G,B intensities
− 3D colour space (cube) with axes R, G and B
− each axis 0 → 1 (below scaled to 0-255 for 1 byte per colour channel)
− Black = (0,0,0) (origin); White = (1,1,1) (opposite corner)
− Problem : difficult to map continuous scalar range to 3D space
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− Problem : difficult to map continuous scalar range to 3D space
� can use subset (e.g. a diagonal axis) but imperfect
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Visualisation : Lecture 5
Colour Spaces - Greyscale� Linear combination of R, G, B
� Defined as linear range
− easy to map linear scalar range to grayscale intensity
− can enhance structural detail in visualisation� The shading effect is emphasized
� as distraction of colour is removed
− not really using full graphics capability
− lose colour associations :
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− e.g. red=bad/hot, green=safe, blue=cold
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Visualisation : Lecture 5
Example� Winding numbers :
− say we have a curve on a 2D plane
− How much a point is surrounded by the curve− How much a point is surrounded by the curve� Winding numbers
− We can calculate the winding numbers at every location
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� 1/4 1 0
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Visualisation : Lecture 5
Winding numbers visualized by grey scale
� Cup with an open top Star with an open top
� A bar
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� A bar
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Visualisation : Lecture 5
Color Spaces – RGB Combining different channels to define a mapping function
B(x) R(x)
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Visualisation : Lecture 5
Winding numbers visualized by red and blue
� Cup with an open top Star with an open top
� A bar
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� A bar
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Visualisation : Lecture 5
Colour spaces - HSV � Colour represented in H,S,V parametrised space
− commonly modelled as a cone
� H (Hue) = dominant wavelength of colour
− colour type {e.g. red, blue, green...}
� S (Saturation) = amount of Hue present
− “vibrancy” or purity of colour
V (Value) = brightness of colour
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� V (Value) = brightness of colour
− brightness of the colour
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Visualisation : Lecture 5
Colour spaces - HSV � HSV Component Ranges
− Hue = 0 → 360o
− Saturation = 0 → 1
� e.g. for Hue≈blue
0.5 = sky colour; 1.0 = primary blue
− Value = 0 → 1 (amount of light)
� e.g. 0 = black, 1 = bright
� All can be scaled to 0 →100% (i.e. min→max)
use hue range for colour gradients
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− use hue range for colour gradients
− very useful for scalar visualisation with colour ma ps
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Visualisation : Lecture 5
Example : HSV image components
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Visualisation : Lecture 5
Winding numbers by HSV (only changing hue)
� Cup with an open top Star with an open top
� A bar
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Visualisation : Lecture 5
Different Colour Spaces� Visualising gas density in a combustion chamber
− Scalar = gas density
− Colour Map =
A BA: grayscale
B: hue range blue to red
C: hue range red to blue
D: specifically designedtransfer function
� highlights contrast
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C D
� highlights contrast
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Visualisation : Lecture 5
Color maps from Matlab
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Visualisation : Lecture 5
Colour Table/Transfer Function Design
� “More of an art than a science”
� Key focus of colour table design
− emphasize important features / distinctions
− minimise extraneous detail
� Often task specific
− consider application (e.g. temperature change, use hue red to blue)
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− consider application (e.g. temperature change, use hue red to blue)
− Rainbow colour maps – rapid change in colour hue
representing a ‘rainbow’ of colours.
shows small gradients well as colours change quickly.
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Visualisation : Lecture 5
Examples of Manually Designed LUT3D Height Data
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HSV based colour transfer function
− continuous transition of height represented
8 colour limited lookup table
− discrete height transitions
− Based on human knowledge
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Visualisation : Lecture 5
Summary
� Discrete data structure
� Topology and interpolation
Topological dimension� Topological dimension
� Data Representation
− Structure � Topology - Cells
� Geometry - points
− Attributes
Taku Komura Colour Mapping 43
− Attributes
� Colour Mapping