Date post: | 12-Jan-2016 |
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Overview of presentation
• Introduction– Moment-based painterly rendering
• Original contributions– Multiscale approach– Parametrized dithering– Image abstraction
• Results, conclusions and future work
Review of MBPR
• Goal: automatically create painting-like images from digital photographs
• Proceed as an artist who progressively strokes a canvas
• Each stroke approximates a neighborhood of the input image
• First step: Analyze input image and compute stroke list
• Second step: Blend strokes together to produce final image
Analysis step
• Determine stroke distribution– More strokes close to high frequencies– Do not allow gaps larger than stroke size
• Compute parameters for each stroke– Color is given by input color at position– Remaining parameters come from image-
moment theory
Stroke distribution
• Stroke area image– For each pixel, shows area of stroke at position– Dark values correspond to small strokes…– ...which in turn correspond to high frequencies
• Stroke positions image– Carefully dithered version of stroke are image– Density inversely proportional to stroke areas– No large empty regions
Stroke area Image
• Dark regions mean smaller strokes, or higher frequencies
• Size of neighborhoods being considered determine range of frequencies captured
Stroke positions image
• High frequencies yield more strokes
• No holes larger than neighborhood size
Stroke parameters
• Position within neighborhood
• Width and Length• Orientation• Color• Template alpha map is
fixed throughout
(xc, yc)L
W
Color distance Image
• Given a color and a neighborhood, compute distance from color to that of each pixel
• Captures the shape of the stroke
Computing stroke parameters
• Color is pixel color at neighborhood center
• Remaining parameters correspond to a rectangle similar to color difference image
Synthesis step
• Blend stroke list together to produce final painted image
QuickTime™ and aTIFF (Uncompressed) decompressorare needed to see this picture.
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What to improve?
• Stroke sizes do not vary all that much– Real color difference images are not high contrast
• Large features must be composed by many strokes– Those that are larger than the neighborhood size
• Too many strokes used to cover all image• Stroke distribution end up being too uniform
How to improve?
• Capture strokes at several different resolutions– How to prevent high-res strokes from
completely overwriting low-res strokes?
• Use a parametrized dithering algorithm– Hi-res strokes gradually concentrate only on
edges
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Multi-resolution
• Use a pyramid of resolutions to capture strokes on wider frequency range
• Blend hi-res levels on top of low-res levels
Parametrized dithering
• Transform area value before dithering
• Diffuse error randomly in all directions
• Parameter e enhances values close to edges
• Parameter s controls stroke spreading limit
• Both parameters are changed within levels
2425 strokes2453 strokes5771 strokes10294 strokes
Varying the parameters
• Empirical formulas adjust dithering parameters as a function of resolution
Com
pari
son
sing
lesc
ale
63933 strokes
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Com
pari
son
mul
tisc
ale
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20883 strokes
• Operations performed are:– rotation, scaling and blending
– color difference image, stroke are image• Performed over small neighborhoods
• Requirements are:– Avoid copy operations
– Avoid memory allocation
– General enough to be used always
– As simple as possible
Image abstraction
Simple structure
• Neighborhood representation is uniform, and shares buffer with original image
• All graphics primitives operates equally in images and neighborhoods
• Clipping logic is isolated in only one function
• No copies needed
Results: gallery
Conclusions
• Multiscale approach can produce images with less strokes and wider frequency range
• Parametrized dithering algorithm provides better control over stroke distribution
• Image abstraction provides good performance and simplifies code
Future work
• Let low-res levels contribution influence stroke parameter computation for higher levels
• Can we achieve photo-realism, or perhaps use ideas to compact image?
• Explore coherence in stroke lists to help NPR animations