IMAGE SEGMENTATION BY UNIFYING REGION AND BOUNDARY INFORMATION

被引：123

作者：

HADDON, JF ^{[1
]}

BOYCE, JF ^{[1
]}

机构：

[1] UNIV LONDON KINGS COLL,WHEATSTONE LAB,DEPT PHYS,LONDON WC2R 2LS,ENGLAND

来源：

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | 1990年 / 12卷 / 10期

关键词：

Computer vision; cooccurrence matrices; edge detection; entropy; image processing; image sequences; infrared; Markov models; relaxation labeling; remote sensing; scene analysis; segmentation; thresholding;

D O I：

10.1109/34.58867

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A two stage method of image segmentation based on gray level cooccurrence matrices is described. An analysis of the distributions within a cooccurrence matrix defines an initial pixel classification into both region and interior or boundary designation. Local consistency of pixel classification is then implemented by minimising the entropy of local information, where region information is expressed via conditional probabilities, estimated from the cooccurrence matrices, and boundary information via conditional probabilities which are determined a priori. The method robustly segments an image into homogeneous areas and generates an edge map. The technique extends easily to general edge operators; an example is given for the Canny operator. Applications to synthetic and forward looking infrared (FLIR) images are given. © 1990 IEEE

引用

页码：929 / 948

页数：20

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