{"title":"Colour and Multispectral Morphological Processing","authors":"J. Serra","doi":"10.1109/ICAPR.2009.109","DOIUrl":null,"url":null,"abstract":"The classical colour polar-based representations (HLS, HSV, etc.) lead to brightness and saturation with non consistent properties. The requirements for a correct quantitative colour polar representation are recalled. They lead to using norms, and in particular the L1 norm. Colour images are multivariable functions, and for segmenting them one must go through reducing step. It is classically obtained by calculating a gradient module,which is then segmented as a gray tone image. An alternative solution is proposed in the paper. It is based on separated segmentations, followed by final merging into a unique partition. The generalization of the top-hat transformation for extracting colour details is also considered. These new marginal colour operators take advantage of an adaptive combination of the chromatic and the achromatic (or the spectral and the spatio-geometric) colour components. Examples in feature extraction from geographical maps are given.","PeriodicalId":443926,"journal":{"name":"2009 Seventh International Conference on Advances in Pattern Recognition","volume":"52 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 Seventh International Conference on Advances in Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAPR.2009.109","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
The classical colour polar-based representations (HLS, HSV, etc.) lead to brightness and saturation with non consistent properties. The requirements for a correct quantitative colour polar representation are recalled. They lead to using norms, and in particular the L1 norm. Colour images are multivariable functions, and for segmenting them one must go through reducing step. It is classically obtained by calculating a gradient module,which is then segmented as a gray tone image. An alternative solution is proposed in the paper. It is based on separated segmentations, followed by final merging into a unique partition. The generalization of the top-hat transformation for extracting colour details is also considered. These new marginal colour operators take advantage of an adaptive combination of the chromatic and the achromatic (or the spectral and the spatio-geometric) colour components. Examples in feature extraction from geographical maps are given.