Extended GrabCut for 3D and RGB-D Point Clouds

被引：0

作者：

Sallem, Nizar K. ^{[1
]}

Devy, Michel ^{[1
]}

机构：

[1] CNRS, LAAS, F-31400 Toulouse, France

来源：

ADVANCED CONCEPTS FOR INTELLIGENT VISION SYSTEMS, ACIVS 2013 | 2013年 / 8192卷

关键词：

segmentation; graph-cut; GrabCut; RGB-D; max-flow;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

GrabCut is a renowned algorithm for image segmentation. It exploits iteratively the combinatorial minimization of energy function as introduced in graph-cut methods, to achieve background foreground classification with fewer user's interaction. In this paper it is proposed to extend GrabCut to carry out segmentation on RGB-D point clouds, based both on appearance and geometrical criteria. It is shown that an hybrid GrabCut method combining RGB and D information, is more efficient than GrabCut based only on RGB or D images.

引用

页码：354 / 365

页数：12

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