{"title":"利用纹理信息进行降噪以增强图像分类","authors":"W. Harron, R. Dony, Stephen Miller","doi":"10.1109/CCECE.2009.5090129","DOIUrl":null,"url":null,"abstract":"A method is presented to classify the percent intramuscular fat (%IMF) for beef cattle using ultrasound imaging. As the images captured tend to include a significant amount of noise a noise reduction algorithmwas used. The effectiveness of using filtered images to calculate texture measures for the classification and prediction of the %IMF is compared to the effectiveness of using unfiltered images.","PeriodicalId":153464,"journal":{"name":"2009 Canadian Conference on Electrical and Computer Engineering","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Noise reduction to enhance classification of images using textural information\",\"authors\":\"W. Harron, R. Dony, Stephen Miller\",\"doi\":\"10.1109/CCECE.2009.5090129\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A method is presented to classify the percent intramuscular fat (%IMF) for beef cattle using ultrasound imaging. As the images captured tend to include a significant amount of noise a noise reduction algorithmwas used. The effectiveness of using filtered images to calculate texture measures for the classification and prediction of the %IMF is compared to the effectiveness of using unfiltered images.\",\"PeriodicalId\":153464,\"journal\":{\"name\":\"2009 Canadian Conference on Electrical and Computer Engineering\",\"volume\":\"16 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-05-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 Canadian Conference on Electrical and Computer Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CCECE.2009.5090129\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 Canadian Conference on Electrical and Computer Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCECE.2009.5090129","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Noise reduction to enhance classification of images using textural information
A method is presented to classify the percent intramuscular fat (%IMF) for beef cattle using ultrasound imaging. As the images captured tend to include a significant amount of noise a noise reduction algorithmwas used. The effectiveness of using filtered images to calculate texture measures for the classification and prediction of the %IMF is compared to the effectiveness of using unfiltered images.
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