{"title":"基于灰度侵蚀的时频浓度判据","authors":"Guang-hui Wang, Hao-chen Wang, Mingshuo Zhu","doi":"10.1109/SIPROCESS.2016.7888292","DOIUrl":null,"url":null,"abstract":"A new criterion is proposed in this paper which employs the grayscale erosion to measure the concentration of different time-frequency representations/distributions. In contrast to some widely used Concentration Measures proposed by L. Stankovic and L. Jones, this method combines the both width and peakedness information of the auto-term area. Moreover, it can be used in the multi-component signal analysis when the minimum mean square error criterion is invalid.","PeriodicalId":142802,"journal":{"name":"2016 IEEE International Conference on Signal and Image Processing (ICSIP)","volume":"308 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A time-frequency concentration criterion using grayscale erosion\",\"authors\":\"Guang-hui Wang, Hao-chen Wang, Mingshuo Zhu\",\"doi\":\"10.1109/SIPROCESS.2016.7888292\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A new criterion is proposed in this paper which employs the grayscale erosion to measure the concentration of different time-frequency representations/distributions. In contrast to some widely used Concentration Measures proposed by L. Stankovic and L. Jones, this method combines the both width and peakedness information of the auto-term area. Moreover, it can be used in the multi-component signal analysis when the minimum mean square error criterion is invalid.\",\"PeriodicalId\":142802,\"journal\":{\"name\":\"2016 IEEE International Conference on Signal and Image Processing (ICSIP)\",\"volume\":\"308 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE International Conference on Signal and Image Processing (ICSIP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SIPROCESS.2016.7888292\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE International Conference on Signal and Image Processing (ICSIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIPROCESS.2016.7888292","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A time-frequency concentration criterion using grayscale erosion
A new criterion is proposed in this paper which employs the grayscale erosion to measure the concentration of different time-frequency representations/distributions. In contrast to some widely used Concentration Measures proposed by L. Stankovic and L. Jones, this method combines the both width and peakedness information of the auto-term area. Moreover, it can be used in the multi-component signal analysis when the minimum mean square error criterion is invalid.
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