Geometric moment invariants

被引：112
|
作者：
Xu, Dong
Li, Hua
机构：
[1] Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100080, Peoples R China
[2] Chinese Acad Sci, Inst Comp Technol, Key Lab Comp Syst & Architecture, Beijing 100080, Peoples R China
[3] Chinese Acad Sci, Inst Comp Technol, Natl Res Ctr Intelligent Comp Syst, Beijing 100080, Peoples R China
来源：
PATTERN RECOGNITION | 2008年 / 41卷 / 01期
基金：
中国国家自然科学基金;
关键词：
geometric primitive; moment invariant; similarity transformation; symbolic computation;
D O I：
10.1016/j.patcog.2007.05.001
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper derives moment invariants in an intuitive way by multiple integrals of invariant geometric primitives like distance, area and volume. Many existing 2-D and 3-D moment invariants are re-expressed to prove the correctness of this method. Furthermore, this construction method can be easily extended to higher dimensional space and get higher-order moment invariants. Explicit expressions of 3-D moment invariants are generated automatically with the help of symbolic computation software and can be regarded as a kind of shape descriptor for the representation of solid objects. (C) 2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码：240 / 249
页数：10
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