Statistical significance of features in digital images

被引：28
作者：
Godtliebsen, F
Marron, JS
Chaudhuri, P
机构：
[1] Indian Stat Inst, Theoret Stat & Math Div, Kolkata 700108, W Bengal, India
[2] Univ Tromso, Dept Math & Stat, N-9037 Tromso, Norway
[3] Univ N Carolina, Dept Stat, Chapel Hill, NC 27599 USA
来源：
IMAGE AND VISION COMPUTING | 2004年 / 22卷 / 13期
关键词：
kernel smoothing; curvature; gradient; scale space; statistical significance; SiZer;
D O I：
10.1016/j.imavis.2004.05.002
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 develops a methodology for finding which features in a noisy image are strong enough to be distinguished from background noise. It is based on scale-space, i.e. a family of smooths of the image. Pixel locations having statistically significant gradient and/or curvature are highlighted by colored symbols. The gradient version is enhanced by displaying regions of significance with streamlines. The usefulness of the new methodology is illustrated by the analysis of simulated and real images. (C) 2004 Elsevier B.V. All rights reserved.
引用
收藏
页码：1093 / 1104
页数：12
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