Imperial College London


Faculty of EngineeringDepartment of Computing

Chair in Machine Learning and Pattern Recognition



m.bronstein Website




569Huxley BuildingSouth Kensington Campus






BibTex format

author = {Litman, R and Bronstein, AM and Bronstein, MM},
doi = {10.1016/j.cag.2011.03.011},
journal = {Computers and Graphics (Pergamon)},
pages = {549--560},
title = {Diffusion-geometric maximally stable component detection in deformable shapes},
url = {},
volume = {35},
year = {2011}

RIS format (EndNote, RefMan)

AB - 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. © 2011 Elsevier Ltd. All rights reserved.
AU - Litman,R
AU - Bronstein,AM
AU - Bronstein,MM
DO - 10.1016/j.cag.2011.03.011
EP - 560
PY - 2011///
SN - 0097-8493
SP - 549
TI - Diffusion-geometric maximally stable component detection in deformable shapes
T2 - Computers and Graphics (Pergamon)
UR -
VL - 35
ER -