On the convergence of fitting algorithms in computer vision

被引：19

作者：

Chernov, N. ^{[1
]}

机构：

[1] Univ Alabama Birmingham, Dept Math, Birmingham, AL 35294 USA

来源：

JOURNAL OF MATHEMATICAL IMAGING AND VISION | 2007年 / 27卷 / 03期

基金：

美国国家科学基金会;

关键词：

statistical methods; maximum likelihood; convergence rates; conic fitting;

D O I：

10.1007/s10851-006-0646-1

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We investigate several numerical schemes for estimating parameters in computer vision problems: HEIV, FNS, renormalization method, and others. We prove mathematically that these algorithms converge rapidly, provided the noise is small. In fact, in just 1-2 iterations they achieve maximum possible statistical accuracy. Our results are supported by a numerical experiment. We also discuss the performance of these algorithms when the noise increases and/or outliers are present.

引用

页码：231 / 239

页数：9

共 16 条

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