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Neural Networks for Signal Processing II Proceedings of the 1992 IEEE Workshop最新文献

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Adaptive decision-feedback equalizer using forward-only counterpropagation networks for Rayleigh fading channels 基于前向反传播网络的瑞利衰落信道自适应决策反馈均衡器
Neural Networks for Signal Processing II Proceedings of the 1992 IEEE Workshop Pub Date : 1992-08-31 DOI: 10.1109/NNSP.1992.253655
R. Kaneda, T. Manabe, S. Fujii
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引用次数: 2
An adaptive neural network model for distinguishing line- and edge detection from texture segregation 一种自适应神经网络模型，用于从纹理分离中区分线和边缘检测
Neural Networks for Signal Processing II Proceedings of the 1992 IEEE Workshop Pub Date : 1900-01-01 DOI: 10.1109/NNSP.1992.253673
M. V. Van Hulle, T. Tollenaere
{"title":"An adaptive neural network model for distinguishing line- and edge detection from texture segregation","authors":"M. V. Van Hulle, T. Tollenaere","doi":"10.1109/NNSP.1992.253673","DOIUrl":"https://doi.org/10.1109/NNSP.1992.253673","url":null,"abstract":"The authors consider an important paradigm in vision: distinguishing object contours or edges (and lines) from object surface textures. To accomplish this, an artificial neural network model, called the EDANN model, is used for both texture segregation and line and edge detection starting from a common bank of spatial filters. The model provides different representations of a retinal image in such a way that different actions and decisions about the presence of objects in the visual scene can be undertaken in a further stage. Three possible cases of distinguishing luminance-defined lines and edges from noise textures are considered.<<ETX>>","PeriodicalId":438250,"journal":{"name":"Neural Networks for Signal Processing II Proceedings of the 1992 IEEE Workshop","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128164638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Neural networks and nonparametric regression 神经网络与非参数回归
Neural Networks for Signal Processing II Proceedings of the 1992 IEEE Workshop Pub Date : 1900-01-01 DOI: 10.1109/NNSP.1992.253661
V. Cherkassky
{"title":"Neural networks and nonparametric regression","authors":"V. Cherkassky","doi":"10.1109/NNSP.1992.253661","DOIUrl":"https://doi.org/10.1109/NNSP.1992.253661","url":null,"abstract":"The problem of estimating an unknown function from a finite number of noisy data points is a problem of fundamental importance for many applications in signal processing, machine vision, pattern recognition, and process control. Recently, several new computational techniques for nonparametric regression have been proposed by statisticians and by researchers in artificial neural networks. The author presents a critical survey and a common taxonomy of statistical and neural network methods for regression. Global parametric methods, piecewise parametric or locally parametric methods, and adaptive computation methods are considered.<<ETX>>","PeriodicalId":438250,"journal":{"name":"Neural Networks for Signal Processing II Proceedings of the 1992 IEEE Workshop","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127451759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 10
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