A COMBINED MARKOV RANDOM-FIELD AND WAVE-PACKET TRANSFORM-BASED APPROACH FOR IMAGE SEGMENTATION

被引：22

作者：

BELLO, MG

机构：

[1] The Charles Stark Draper Laboratory, Cambridge

来源：

IEEE TRANSACTIONS ON IMAGE PROCESSING | 1994年 / 3卷 / 06期

关键词：

D O I：

10.1109/83.336251

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The present work formulates a novel segmentation algorithm which combines the use of Markov random field models for image-modeling with the use of the discrete wave-packet transform for image analysis. Image segmentations are derived and refined at a sequence of resolution levels, using as data selected wave-packet transform images or ''channels.'' The described segmentation algorithm is compared with non-multiresolution Markov random field-based image segmentation algorithms in the contest of synthetic image example problems, and found to be both significantly more efficient and effective.

引用

页码：834 / 846

页数：13

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