Bayesian multiscale analysis of images modeled as Gaussian Markov random fields

被引：14

作者：

Thon, Kevin ^{[1
]}

Rue, Havard ^{[2
]}

Skrovseth, Stein Olav ^{[1
]}

Godtliebsen, Fred ^{[3
]}

机构：

[1] Univ Hosp N Norway, Norwegian Ctr Integrated Care & Telemed, N-9038 Tromso, Norway

[2] Norwegian Univ Sci & Technol, Dept Math Sci, N-7491 Trondheim, Norway

[3] Univ Tromso, Dept Math & Stat, N-9037 Tromso, Norway

来源：

COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2012年 / 56卷 / 01期

关键词：

Scale space; Multi-resolution analysis; Bayesian analysis; Gaussian Markov random fields; SCALE-SPACE; FEATURES; SEGMENTATION; DERMOSCOPY; DIAGNOSIS; ACCURACY; SURFACES; MELANOMA; SIGNALS;

D O I：

10.1016/j.csda.2011.07.009

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

A Bayesian multiscale technique for the detection of statistically significant features in noisy images is proposed. The prior is defined as a stationary intrinsic Gaussian Markov random field on a toroidal graph, which enables efficient computation of the relevant posterior marginals. Hence the method is applicable to large images produced by modern digital cameras. The technique is demonstrated in two examples from medical imaging. (C) 2011 Elsevier B.V. All rights reserved.

引用

页码：49 / 61

页数：13

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