Publications

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“ARDECO: Automatic Region DEtection and Conversion”
Gregory Lecot and Bruno Lévy
Eurographics Symposium on Rendering, 2006

Abstract: We present Ardeco, a new algorithm for image abstraction and conversion from bitmap images into vector graphics. Given a bitmap image, our algorithm automatically computes the set of vector primitives and gradients that best approximates the image. In addition, more details can be generated in user-selected important regions, defined from eye-tracking data or from an importance map painted by the user. Our algorithm is based on a new two-level variational parametric segmentation algorithm, minimizing Mumford and Shah's energy and operating on an intermediate triangulation, well adapted to the features of the image.

BibTex reference

@INPROCEEDINGS{lecot:ARD:2006,
   AUTHOR     = "Gregory Lecot and Bruno Lévy",
   TITLE      = "ARDECO: Automatic Region DEtection and Conversion",
   BOOKTITLE  = "Eurographics Symposium on Rendering",
   YEAR       = "2006",
}

Supplemental material, links, hindsight ...

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Follow this link to supplemental material.

Source code

The complete source-code of ARDECO and a compiled version for Windows are available as a plug-in for our Graphite software, see the Graphite page.

Examples

These web-pages contain in the VECTOR IMAGE GALLERIES section all the examples and results shown in the paper, and some more that did not fit due to space considerations.

All the image galleries are available in two versions, one with all the results in the SVG format (this requires a SVG plugin, available from there), and one with standard images only. The SVG file format is becoming a standard, but is not supported yet by all internet browsers. If you have the SVG plugin, then you can zoom in the results by right-clicking on the vector images. In both cases, to let the reader experience resolution-independent graphics, each example has a link to a PDF version, that can be viewed/zoomed-in/printed in Acrobat reader or in any PDF-compliant software.

The VIDEOS section shows the principal steps of the algorithm.

We have included in the SOURCE-CODE section the complete implementation of our generic rasterizer and M-estimator fitting, for bivariate polynomial functions of degrees from 0 to 3.

 

Links and references, further reading

Thanks to Alexander Belyaev (MPII Saarebruck) for the following links:
  • L. Demaret, N. Dyn, and A. Iske: Image Compression by Linear Splines over Adaptive Triangulations. Signal Processing 86(7), July 2006, 1604-1616. see this link .
  • Roman Kazinnik, Shai Dekel and Nira Dyn, Low-bit rate image coding using adaptive geometric piecewise polynomial approximation, see this link .