A Markov Random Fields model for hybrid edge- and region-based color image segmentation

被引：4

作者：

Wesolkowski, S ^{[1
]}

Fieguth, P ^{[1
]}

机构：

[1] Univ Waterloo, Waterloo, ON N2L 3G1, Canada

来源：

IEEE CCEC 2002: CANADIAN CONFERENCE ON ELECTRCIAL AND COMPUTER ENGINEERING, VOLS 1-3, CONFERENCE PROCEEDINGS | 2002年

关键词：

D O I：

10.1109/CCECE.2002.1013070

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, a framework based on a Markov Random Field approach for color image segmentation enhanced by edge detection is presented, We use a previously developed methodology to transform the image into an R'G'B' space to remove any highlight components preserving the vector-angle component, representing color hue but not intensity, to remove shading effects. To improve the segmentation process we describe the idea of a line process. This allows for the integration of region segmentation with edge detection in a Markov Random Field framework. We discuss the advantages of this new model with respect to the previously developed image segmentation model.

引用

页码：945 / 949

页数：3

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