Unsupervised image segmentation with the self-organizing map and statistical methods

被引：3

作者：

Iivarinen, J ^{[1
]}

Visa, A ^{[1
]}

机构：

[1] Helsinki Univ Technol, Lab Comp & Informat Sci, FIN-02015 Helsinki, Finland

来源：

INTELLIGENT ROBOTS AND COMPUTER VISION XVII: ALGORITHMS, TECHNIQUES, AND ACTIVE VISION | 1998年 / 3522卷

关键词：

image segmentation; unsupervised segmentation; self-organizing map; statistical self-organizing map; density estimation;

D O I：

10.1117/12.325796

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 special type of image segmentation, a two-class segmentation, is considered. Defect detection in quality control applications is a typical two-class problem. The main idea in this paper is to train the two-class classifier with fault-free samples that is an unexpected approach. The reason is that defects are rare and expensive. The proposed defect detection is based on the following idea: an unknown sample is classified as a defect if it differs enough from the estimated prototypes of fault-free samples. The self-organizing map is used to estimate these prototypes. Surface images are used to demonstrate the proposed image segmentation procedure.

引用

页码：516 / 526

页数：11

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