Non-Photorealistic Computer Graphics Library

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Found 117 item(s) authored in "2005".
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Article Erratum: Synthesis of facial caricature using eigenspaces
Guang Zhe Xu, Masahide Kaneko, Akira Kurematsu.
Electronics and Communications in Japan (Part III: Fundamental Electronic Science), Vol. 88, No. 2, pp. 64--76, February, 2005. [BibTeX]

Article Example-Based Color Stylization of Images
Youngha Chang, Suguru Saito, Keiji Uchikawa, Masayuki Nakajima.
ACM Transactions on Applied Perception (TAP), Vol. 2, No. 3, pp. 322--345, July, 2005. [BibTeX]

Proceedings Example-based color transformation for image and video
Youngha Chang, Suguru Saito, Masayuki Nakajima.
3rd International Conference on Computer Graphics and Interactive Techniques in Australasia and South East Asia (GRAPHITE'05), pp. 347--353, Dunedin, New Zealand, 2005. [BibTeX]

Proceedings Example-based Volume Illustrations
Aidong Lu, David Ebert.
IEEE Visualization 2005, 2005. [BibTeX]

Technical Report Expressive Distortion of Strokes and 3D Meshes
Petra Neumann, Tobias Isenberg, M. Sheelagh T. Carpendale, Thomas Strothotte.
Department of Computer Science, University of Calgary, No. Technical Report 2005-776-07, Canada, March, 2005. [BibTeX]

Proceedings Expressive Line Selection by Example

Author(s): Erik Lum, Kwan-Liu Ma.
Proceedings: 13th Pacific Conference on Computer Graphics and Applications (PG'05), 2005.
[BibTeX] Find this paper on Google

An important problem in computer generated line drawing is determining which set of lines produces a representation that is in agreement with a user's communication goals. We describe a method that enables a user to intuitively specify which types of lines should appear in rendered images. Our method employs conventional silhouette-edge and other feature-line extraction algorithms to derive a set of candidate lines, and integrates machine learning into a user-directed line removal process using a sketching metaphor. The method features a simple and intuitive user interface that provides interactive control over the resulting line selection criteria and can be easily adapted to work in conjunction with existing line detection and rendering algorithms. Much of the method's power comes from its ability to learn the relationships between numerous geometric attributes that define a line style. Once learned, a user's style and intent can be passed from object to object as well as from view to view.

Proceedings Fast Techniques for Mosaic Rendering
G. Gallo, G. Di Blasi, M. Petralia.
EuroGraphics Workshop on Computational Aesthetics in Graphics, Visualization and Imaging (CA'05), pp. 29--39, Girona, Spain, 18-20 May, 2005. [BibTeX]

Article Feature- and region-based auto painting for 2D animation
Jie Qiu, Hock Soon Seah, Feng Tian, Zhongke Wu, Quan Chen.
The Visual Computer, Vol. 21, No. 11, pp. 928--944, October, 2005. [BibTeX]

Technical Report G-Strokes: A Concept for Simplifying Line Stylization
Tobias Isenberg, Angela Brennecke.
Department of Computer Science, University of Calgary, No. 2005-780-11, Canada, April, 2005. [BibTeX]

Proceedings Genetic Paint: A Search for Salient Paintings
John P. Collomosse, Peter M. Hall.
EvoMUSART (at EuroGP), Springer Lecture Notes in Computer Science, Vol. 3449, pp. 437--447, Lausanne, March, 2005. [BibTeX]

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