STRUCTURAL MATCHING IN COMPUTER VISION USING PROBABILISTIC RELAXATION

被引：303

作者：

CHRISTMAS, WJ

KITTLER, J

PETROU, M

机构：

[1] Vision, Speech, and Signal Processing Group, Department of Electronic and Electrical Engineering, University of Surrey, Guildford

来源：

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | 1995年 / 17卷 / 08期

关键词：

MATCHING; PROBABILISTIC RELAXATION; OBJECT RECOGNITION;

D O I：

10.1109/34.400565

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, we develop the theory of probabilistic relaxation for matching features extracted from 2D images, derive as limiting cases the various heuristic formulae used by researchers in matching problems, and state the conditions under which they apply. We successfully apply our theory to the problem of matching and recognizing aerial road network images based on road network models and to the problem of edge matching in a stereo pair. For this purpose, each line network is represented by an attributed relational graph where each node is a straight line segment characterized by certain attributes and related with every other node via a set of binary relations.

引用

页码：749 / 764

页数：16

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