Vision Experiments with Neural Deformable Template Matching

被引：4
|
作者：
D.S. Banarse
A.W.G. Duller
机构：
[1] University College of North Wales,School of Electronic Engineering Sciende
来源：
Neural Processing Letters | 1997年 / 5卷 / 2期
关键词：
computer vision; neural network; object recognition; on-line learning; pattern classification; self-organisation;
D O I：
10.1023/A:1009661908220
中图分类号：
学科分类号：
摘要：
This paper describes a neural network architecture that has been developed to perform deformation tolerant object recognition from grey-scale images. It uses a form of deformable template matching, generating new templates in a self-organising manner. The results demonstrate the network's ability to build classes when no suitable classes are available. The amount of deformation allowed within a class can be controlled to allow the network to be applied to a wide range of applications. Results are presented for a set of generated images which allow the effects of the selection of the major network parameters to be shown.
引用
收藏
页码：41 / 49
页数：8
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