Geometric and Photometric Data Fusion in Non-Rigid Shape Analysis

被引：8

作者：

Kovnatsky, Artiom ^{[1
]}

Raviv, Dan ^{[3
]}

Bronstein, Michael M. ^{[1
]}

Bronstein, Alexander M. ^{[2
]}

Kimmel, Ron ^{[3
]}

机构：

[1] Univ Svizzera Italiana, Fac Informat, Inst Computat Sci, Lugano, Switzerland

[2] Tel Aviv Univ, Sch Elect Engn, IL-69978 Tel Aviv, Israel

[3] Technion Israel Inst Technol, Dept Comp Sci, IL-32000 Haifa, Israel

来源：

NUMERICAL MATHEMATICS-THEORY METHODS AND APPLICATIONS | 2013年 / 6卷 / 01期

关键词：

Laplace-Beltrami operator; diffusion equation; heat kernel descriptors; 3D shape retrieval; deformation invariance; INVARIANT; RECOGNITION; RETRIEVAL; FRAMEWORK;

D O I：

10.4208/nmtma.2013.mssvm11

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

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 construction is based on the definition of a diffusion process on the shape manifold embedded into a high-dimensional space where the embedding coordinates represent the photometric information. Experimental results show that such data fusion is useful in coping with different challenges of shape analysis where pure geometric and pure photometric methods fail.

引用

页码：199 / 222

页数：24

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