8 citations · 18 across the 5 of their papers we have counts for
6 papers
MADMM: a generic algorithm for non-smooth optimization on manifolds
Artiom Kovnatsky, Klaus Glashoff, Michael M. Bronstein
Numerous problems in machine learning are formulated as optimization with manifold constraints. In this paper, we propose the Manifold alternating directions method of multipliers…
Functional correspondence by matrix completion
Artiom Kovnatsky, Michael M. Bronstein, Xavier Bresson +1
In this paper, we consider the problem of finding dense intrinsic correspondence between manifolds using the recently introduced functional framework. We pose the functional corres…
Structure-preserving color transformations using Laplacian commutativity
Davide Eynard, Artiom Kovnatsky, Michael M. Bronstein
Mappings between color spaces are ubiquitous in image processing problems such as gamut mapping, decolorization, and image optimization for color-blind people. Simple color transfo…
Coupled quasi-harmonic bases
A. Kovnatsky, M. M. Bronstein, A. M. Bronstein +2
The use of Laplacian eigenbases has been shown to be fruitful in many computer graphics applications. Today, state-of-the-art approaches to shape analysis, synthesis, and correspon…
SHREC 2011: robust feature detection and description benchmark
E. Boyer, A. M. Bronstein, M. M. Bronstein +15
Feature-based approaches have recently become very popular in computer vision and image analysis applications, and are becoming a promising direction in shape retrieval. SHREC'11 r…
Diffusion framework for geometric and photometric data fusion in non-rigid shape analysis
Artiom Kovnatsky, Michael M. Bronstein, Alexander M. Bronstein +1
In this paper, we explore the use of the diffusion geometry framework for the fusion of geometric and photometric information in local and global shape descriptors. Our constructio…