{"title":"Hysteresis quantizer","authors":"K. Jin'no, M. Tanaka","doi":"10.1109/ISCAS.1997.608919","DOIUrl":null,"url":null,"abstract":"This paper proposes two type quantizers by using mutual connected neural networks. Since each cell of the neural networks has hysteresis properties, these quantizers can convert any input signals into a suitable quantization output. Also, we propose its application for image processing which can be intensity conversion. By using an area intensity method, we can get high quality output images in spite of to use bilevel output function.","PeriodicalId":68559,"journal":{"name":"电路与系统学报","volume":"34 1","pages":"661-664 vol.1"},"PeriodicalIF":0.0000,"publicationDate":"2000-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"电路与系统学报","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.1109/ISCAS.1997.608919","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
This paper proposes two type quantizers by using mutual connected neural networks. Since each cell of the neural networks has hysteresis properties, these quantizers can convert any input signals into a suitable quantization output. Also, we propose its application for image processing which can be intensity conversion. By using an area intensity method, we can get high quality output images in spite of to use bilevel output function.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
