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# 03raster 1

## by Ketan Jani on Apr 13, 2011

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## 03raster 1Presentation Transcript

• Raster Graphics 고려대학교 컴퓨터 그래픽스 연구실
• Contents
• Display Hardware
• How are images display?
• Raster Graphics Systems
• How are imaging system organized
• Output Primitives
• How can we describe shapes with primitives?
• Color Models
• How can we describe and represent colors?
• Bresenham’s Line Algorithm
• Accurate and Efficient
• Use only incremental integer calculations
• Test the sign of an integer parameter
• Case) Positive Slope Less Than 1
• After the pixel ( x k , y k ) is displayed,
• next which pixel is decided to plot
• in column x k +1 ?
•  ( x k +1 , y k ) or ( x k +1 , y k +1 )
x k y k x k +1 y k +1
• Bresenham’s Algorithm(cont.)
• Case) Positive Slope Less Than 1
• y at sampling position x k
• Difference
• Decision parameter
d 1 – d 2 < 0  ( x k +1, y k ) d 1 – d 2 > 0  ( x k +1, y k +1) d 1 d 2 x k y k x k +1 y k +1
• Bresenham’s Algorithm(cont.)
• Case) Positive Slope Less Than 1
• Decision parameter
• Decision parameter of a starting pixel ( x 0 , y 0 )
• Bresenham’s Algorithm(cont.)
• Algorithm for 0< m <1
• Input the two line endpoints and store the left end point in ( x 0 , y 0 )
• Load ( x 0 , y 0 ) into the frame buffer; that is, plot the first point
• Calculate constants Δ x , Δ y , 2 Δ y , and 2 Δ y− 2 Δ x , and obtain the starting value for the decision parameter as
• At each x k along the line, start at k =0 , perform the following test:
• If p k < 0 , the next point to plot is ( x k +1 , y k ) and
• Otherwise, the next point to plot is ( x k +1 , y k +1 ) and
• Repeat step 4 Δ x times
• Polygons
• Filling Polygons
• Scan-line fill algorithm
• Inside-Outside tests
• Boundary fill algorithm
1 2 3 4 5 6 7 8 9 1 2 3 4 6 7 8 9 10 11 5
• Scan-Line Polygon Fill
• Topological Difference between 2 Scan lines
• y : intersection edges are opposite sides
• y’ : intersection edges are same side
y y’ 1 2 1 1 2
• Scan-Line Polygon Fill (cont.)
• Edge Sorted Table
C C’ B D E A 0 1 y A y D y C Scan-Line Number y E x A 1/m AE y B x A 1/m AB y C’ x D 1/m DC y E x D 1/m DE y B x C 1/m CB
• Inside-Outside Tests
• Self-Intersections
• Odd-Even rule
• Nonzero winding number rule
exterior interior
• Boundary-Fill Algorithm
• Proceed to Neighboring Pixels
• 4-Connected
• 8-Connected
• Antialiasing
• Aliasing
• Undersampling: Low-frequency sampling
• Nyquist sampling frequency:
• Nyquist sampling interval:
original sample reconstruct
• Antialiasing (cont.)
• Supersampling (Postfiltering)
• Area Sampling (Prefiltering)
• Pixel Phasing
• Shift the display location of pixel areas
• Micropositioning the electron beam in relation to object geometry
• Supersampling
• Subpixels
• Increase resolution
10 11 12 20 21 22 (10, 20): Maximum Intensity (11, 21): Next Highest Intensity (11, 20): Lowest Intensity
• Supersampling
• Subpixels
• Increase resolution
10 11 12 20 21 22 (10, 20): Maximum Intensity (11, 21): Next Highest Intensity (11, 20): Lowest Intensity
• Give More Weight to Subpixels Near the Center of a Pixel Area
1 2 1 2 4 2 1 2 1
• Area Sampling
• Set Each Pixel Intensity Proportional to the Area of Overlap of Pixel
• 2 adjacent vertical (or horizontal) screen grid lines  trapezoid
10 11 12 20 21 22 (10, 20): 90% (10, 21): 15%
• Filtering Techniques
• Filter Functions (Weighting Surface)
Box Filter Cone Filter Gaussian Filter
• Contents
• Display Hardware
• How are images display?
• Raster Graphics Systems
• How are imaging system organized?
• Output Primitives
• How can we describe shapes with primitives?
• Color Models
• How can we describe and represent colors?
• Electromagnetic Spectrum
• Visible Light Frequencies Range between
• Red: 4.3 x 10 14 hertz (700nm)
• Violet: 7.5 x 10 14 hertz (400nm)
• Visible Light
• The Color of Light is Characterized by
• Hue: dominant frequency (highest peak)
• Saturation: excitation purity (ratio of highest to rest)
• Brightness: luminance (area under curve)
White Light Orange Light
• Color Perception
• Tristimulus Theory of Color
• Spectral-response functions of each of the three types of cones on the human retina
• Color Models
• RGB
• XYZ
• CMY
• HSV
• Others
• RGB Color Model
R G B Color 0.0 0.0 0.0 Black 1.0 0.0 0.0 Red 0.0 1.0 0.0 Green 1.0 1.0 0.0 Yellow 1.0 0.0 1.0 Magenta 0.0 1.0 1.0 Cyan 1.0 1.0 1.0 White 0.0 0.0 1.0 Blue
• RGB Color Cube
• RGB Spectral Colors
• Amounts of RGB Primaries Needed to Display Spectral Colors
• XYZ Color Model (CIE)
• Amounts of CIE Primaries Needed to Display Spectral Colors
• CIE Chromaticity Diagram
• Normalized Amounts of X and Y for Colors in Visible Spectrum
(white)
• CIE Chromaticity Diagram Define Color Gamuts Represent Complementary Color Determine Dominant Wavelength and Purity
• RGB C o lor Gamut
• Color Gamut for a Typical RGB Computer Monitor
(red) (green) (blue)
• CMY Color Model
• Colors are Subtractive
C M Y Color 0.0 0.0 0.0 White 1.0 0.0 0.0 Cyan 0.0 1.0 0.0 Magenta 1.0 1.0 0.0 Blue 1.0 0.0 1.0 Green 0.0 1.0 1.0 Red 1.0 1.0 1.0 Black 0.0 0.0 1.0 Yellow
• CMY Color Cube
• HSV Color Model
• Select a Spectral C o lor (Hue) and the Amount of White (Saturation) and Black (Value)
• HSV Color Model H S V Color 0 1.0 1.0 Red 60 1.0 1.0 Yellow 120 1.0 1.0 Green 240 1.0 1.0 Blue 300 1.0 1.0 Magenta * 0.0 1.0 White * 0.0 0.5 Gray 180 1.0 1.0 Cyan * * 0.0 Black
• HSV Color Model
• Cross Section of the HSV Hexcone