A mixture model for representing shape variation

被引：142

作者：

Cootes, TF ^{[1
]}

Taylor, CJ ^{[1
]}

机构：

[1] Univ Manchester, Dept Med Biophys, Manchester M13 9PT, Lancs, England

来源：

IMAGE AND VISION COMPUTING | 1999年 / 17卷 / 08期

基金：

英国工程与自然科学研究理事会;

关键词：

deformable templates; statistical shape models;

D O I：

10.1016/S0262-8856(98)00175-9

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The shape variation displayed by a class of objects can be represented as probability density function, allowing us to determine plausible and implausible examples of the class. Given a training set of example shapes we can align them into a common co-ordinate frame and use kernel-based density estimation techniques to represent this distribution. Such an estimate is complex and expensive, so we generate a simpler approximation using a mixture of gaussians. We show how to calculate the distribution, and how it can be used in image search to locate examples of the modelled object in new images. (C) 1999 Elsevier Science B.V. All rights reserved.

引用

页码：567 / 573

页数：7

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