Multiscale moment based painterly rendering
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Multiscale Moment-Based Painterly Rendering. Diego Nehab and Luiz Velho [email protected] [email protected] Overview of presentation. Introduction Moment-based painterly rendering Original contributions Multiscale approach Parametrized dithering Image abstraction

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Presentation Transcript

Overview of presentation
Overview of presentation

  • Introduction

    • Moment-based painterly rendering

  • Original contributions

    • Multiscale approach

    • Parametrized dithering

    • Image abstraction

  • Results, conclusions and future work


Review of mbpr
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
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 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
Stroke area Image

  • Dark regions mean smaller strokes, or higher frequencies

  • Size of neighborhoods being considered determine range of frequencies captured


Stroke positions image
Stroke positions image

  • High frequencies yield more strokes

  • No holes larger than neighborhood size


Stroke parameters
Stroke parameters

  • Position within neighborhood

  • Width and Length

  • Orientation

  • Color

  • Template alpha map is fixed throughout

W

(xc, yc)

L


Color distance image
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
Computing stroke parameters

  • Color is pixel color at neighborhood center

  • Remaining parameters correspond to a rectangle similar to color difference image


Synthesis step
Synthesis step

  • Blend stroke list together to produce final painted image


What to improve
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
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


Multi resolution
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
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


Varying the parameters

10294 strokes

2425 strokes

2453 strokes

5771 strokes

Varying the parameters

  • Empirical formulas adjust dithering parameters as a function of resolution


Comparison singlescale

63933 strokes

Comparisonsinglescale


Comparison multiscale

20883 strokes

Comparisonmultiscale


Image abstraction
Image abstraction

  • 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


Simple structure
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



Conclusions
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
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


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