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]
Abstract:
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.
Hardware-Accelerated Parallel Non-Photorealistic Volume Rendering
Erik Lum, Kwan-Liu Ma.
2nd International Symposium on Non-Photorealistic Animation and Rendering (NPAR'02), Annecy, France, June 3-5,
2002. [BibTeX]
Non-Photorealistic Rendering using Watercolor Inspired Textures and Illumination
Erik Lum, Kwan-Liu Ma.
9th Pacific Conference on Computer Graphics and Applications (PG'01), October 16-18,
2001. [BibTeX]
Visualization of Multidimensional, Multivariate Volume Data Using Hardware-Accelerated Non-Photorealistic Rendering Techniques
Aleksander Stompel, Erik Lum, Kwan-Liu Ma.
10th Pacific Conference on Computer Graphics and Applications (PG'02), pp. 394, Tsinghua University, Beijing, October 09 - 11,
2002. [BibTeX]