A neuro-expert architecture for object recognition

J. Selinsky, A. Guez, J. Eilbert, M. Kam
{"title":"A neuro-expert architecture for object recognition","authors":"J. Selinsky, A. Guez, J. Eilbert, M. Kam","doi":"10.1109/IJCNN.1989.118315","DOIUrl":null,"url":null,"abstract":"Summary form only given, as follows. A report is presented on results of experiments in object recognition with a combined neural network/expert system architecture (neuro-expert). The neuro-expert architecture is outlined with a description of the experimental object recognition system. Results are reported for the recognition of a 20-pattern prototype set of synthesized binary images placed at arbitrary rotations. A 100% recognition rate was obtained under noiseless conditions. Addition of 1% and 2% random pixel noise resulted in recognition rates of 95.2% and 89.5%, respectively.<<ETX>>","PeriodicalId":199877,"journal":{"name":"International 1989 Joint Conference on Neural Networks","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1989-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International 1989 Joint Conference on Neural Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IJCNN.1989.118315","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Summary form only given, as follows. A report is presented on results of experiments in object recognition with a combined neural network/expert system architecture (neuro-expert). The neuro-expert architecture is outlined with a description of the experimental object recognition system. Results are reported for the recognition of a 20-pattern prototype set of synthesized binary images placed at arbitrary rotations. A 100% recognition rate was obtained under noiseless conditions. Addition of 1% and 2% random pixel noise resulted in recognition rates of 95.2% and 89.5%, respectively.<>
一个用于对象识别的神经专家架构
仅给出摘要形式,如下。本文报道了基于神经网络/专家系统(neural -expert)结构的物体识别实验结果。概述了实验对象识别系统的神经专家体系结构。结果报告了一个20模式的原型集的合成二值图像的识别放置在任意旋转。在无噪声条件下,识别率达到100%。加入1%和2%随机像素噪声后,识别率分别达到95.2%和89.5%
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信