C. Castiello, G. Castellano, L. Caponetti, A. Fanelli
{"title":"图像像素的模糊分类","authors":"C. Castiello, G. Castellano, L. Caponetti, A. Fanelli","doi":"10.1109/ISP.2003.1275817","DOIUrl":null,"url":null,"abstract":"We present a neuro-fuzzy approach for classification of image pixels into three classes: contour, regular or texture points. Exploiting the processing capabilities of a neural network, fuzzy classification rules are derived by learning from data and applied to classify pixels in grey-level images. To derive a proper set of training data, the spatial properties of the image features and a multiscaled representation of images are considered. The effectiveness of the proposed approach is illustrated on some sample images.","PeriodicalId":285893,"journal":{"name":"IEEE International Symposium on Intelligent Signal Processing, 2003","volume":"208 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Fuzzy classification of image pixels\",\"authors\":\"C. Castiello, G. Castellano, L. Caponetti, A. Fanelli\",\"doi\":\"10.1109/ISP.2003.1275817\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a neuro-fuzzy approach for classification of image pixels into three classes: contour, regular or texture points. Exploiting the processing capabilities of a neural network, fuzzy classification rules are derived by learning from data and applied to classify pixels in grey-level images. To derive a proper set of training data, the spatial properties of the image features and a multiscaled representation of images are considered. The effectiveness of the proposed approach is illustrated on some sample images.\",\"PeriodicalId\":285893,\"journal\":{\"name\":\"IEEE International Symposium on Intelligent Signal Processing, 2003\",\"volume\":\"208 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2003-09-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE International Symposium on Intelligent Signal Processing, 2003\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISP.2003.1275817\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE International Symposium on Intelligent Signal Processing, 2003","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISP.2003.1275817","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We present a neuro-fuzzy approach for classification of image pixels into three classes: contour, regular or texture points. Exploiting the processing capabilities of a neural network, fuzzy classification rules are derived by learning from data and applied to classify pixels in grey-level images. To derive a proper set of training data, the spatial properties of the image features and a multiscaled representation of images are considered. The effectiveness of the proposed approach is illustrated on some sample images.
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
