{"title":"多维信号中多方位和多运动的估计","authors":"I. Stuke, E. Barth, C. Mota","doi":"10.1109/SIBGRAPI.2006.15","DOIUrl":null,"url":null,"abstract":"The estimation of multiple orientations in multidimensional signals is a strongly non-linear problem to which a two-step solution is here presented. First, the problem is linearized by introducing the so-called mixed-orientation parameters as a unique, albeit implicit, descriptor of the orientations. Second, the non-linearities are decomposed such as to find the individual orientations. For two-dimensional signals, e.g., images, this decomposition step is solved by simply determining the roots of a polynomial. For multi-dimensional signals, the nD decomposition problem is solved by reducing it to a cascade of 2D decomposition problems. In this way, a full solution for the estimation of any number of orientations in any dimension is achieved for the first time","PeriodicalId":253871,"journal":{"name":"2006 19th Brazilian Symposium on Computer Graphics and Image Processing","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Estimation of Multiple Orientations and Multiple Motions in Multi-Dimensional Signals\",\"authors\":\"I. Stuke, E. Barth, C. Mota\",\"doi\":\"10.1109/SIBGRAPI.2006.15\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The estimation of multiple orientations in multidimensional signals is a strongly non-linear problem to which a two-step solution is here presented. First, the problem is linearized by introducing the so-called mixed-orientation parameters as a unique, albeit implicit, descriptor of the orientations. Second, the non-linearities are decomposed such as to find the individual orientations. For two-dimensional signals, e.g., images, this decomposition step is solved by simply determining the roots of a polynomial. For multi-dimensional signals, the nD decomposition problem is solved by reducing it to a cascade of 2D decomposition problems. In this way, a full solution for the estimation of any number of orientations in any dimension is achieved for the first time\",\"PeriodicalId\":253871,\"journal\":{\"name\":\"2006 19th Brazilian Symposium on Computer Graphics and Image Processing\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-12-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 19th Brazilian Symposium on Computer Graphics and Image Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SIBGRAPI.2006.15\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 19th Brazilian Symposium on Computer Graphics and Image Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIBGRAPI.2006.15","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Estimation of Multiple Orientations and Multiple Motions in Multi-Dimensional Signals
The estimation of multiple orientations in multidimensional signals is a strongly non-linear problem to which a two-step solution is here presented. First, the problem is linearized by introducing the so-called mixed-orientation parameters as a unique, albeit implicit, descriptor of the orientations. Second, the non-linearities are decomposed such as to find the individual orientations. For two-dimensional signals, e.g., images, this decomposition step is solved by simply determining the roots of a polynomial. For multi-dimensional signals, the nD decomposition problem is solved by reducing it to a cascade of 2D decomposition problems. In this way, a full solution for the estimation of any number of orientations in any dimension is achieved for the first time