@article{92febd90677e419c8d981cbc9adb1150,
title = "Diffusion-geometric maximally stable component detection in deformable shapes",
abstract = "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.",
keywords = "Component tree, Deformable shapes, Diffusion geometry, Feature detection, Level sets, MSER",
author = "Roee Litman and Bronstein, {Alexander M.} and Bronstein, {Michael M.}",
note = "Funding Information: Michael Bronstein is supported by the Swiss High-Performance and High-Productivity Computing (HP2C). ",
year = "2011",
month = jun,
doi = "10.1016/j.cag.2011.03.011",
language = "אנגלית",
volume = "35",
pages = "549--560",
journal = "Computers and Graphics (Pergamon)",
issn = "0097-8493",
publisher = "Elsevier Ltd.",
number = "3",
}