יום שלישי, 17.5.2011, 11:30
חדר 337, בניין טאוב למדעי המחשב
Maximally stable component detection is a very popular method for feature analysis in images, mainly due to its low computation cost and high repeatability.
With the recent advance of feature-based methods in geometric shape analysis, there is significant interest in finding analogous approaches in the 3D world.
In this paper, we formulate a diffusion-geometric framework for stable component detection in non-rigid 3D shapes, which can be used for geometric feature detection and description.
A quantitative evaluation of our method on the SHREC'10 feature detection benchmark shows its potential as a source of high-quality features.