.png){kind=icon}
Return
Expressive line selection by example
.png){kind=icon}
PRE
AI
DOI：10.1007/s00371-005-0342-y.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
non-photorealistic rendering
silhouettes
example-based rendering
machine learning
Journal
IF:
2.9
Papers:
4.6K
Citations:
6.5K
Organization
No organization information available
Cited Papers
ShareSaveShareSave
ShareSave
ShareSaveRuthenium oxide-based nanocomposites with high specific surface area and improved capacitance as a supercapacitor.png)
RSC Adv.
RSC Adv.IF0
PREAI
ShareSaveShareSaveShareSaveTetrahydrobiopterin deficiency and dopamine loss in a genetic mouse model of Lesch‐Nyhan disease
PREAI
ShareSave
View more