A probabilistic approach for foreground and shadow segmentation in monocular image sequences

被引：19

作者：

Wang, Y ^{[1
]}

Tan, T

Loe, KF

Wu, JK

机构：

[1] Inst Infocomm Res, Singapore 119613, Singapore

[2] Natl Univ Singapore, Dept Comp Sci, Singapore 117543, Singapore

[3] Curtin Univ Technol, Dept Comp, Bentley, WA 6102, Australia

来源：

PATTERN RECOGNITION | 2005年 / 38卷 / 11期

关键词：

Bayesian network; foreground segmentation; graphical model; Markov random field; shadow detection;

D O I：

10.1016/j.patcog.2005.02.006

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 presents a novel method of foreground and shadow segmentation in monocular indoor image sequences. The models of background, edge information, and shadow are set up and adaptively updated. A Bayesian network is proposed to describe the relationships among the segmentation label, background, intensity, and edge information. A maximum a posteriori-Markov random field estimation is used to boost the spatial connectivity of segmented regions. (c) 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

引用

页码：1937 / 1946

页数：10

共 25 条

[1] THE THEORY AND PRACTICE OF BAYESIAN IMAGE LABELING [J].