{"title":"带形状标记的分水岭岩石图像分割","authors":"A. Amankwah, C. Aldrich","doi":"10.1109/AIPR.2010.5759719","DOIUrl":null,"url":null,"abstract":"We propose a method for the creation of object markers used in watershed segmentation of rock images. First, we use adaptive thresholding to segment the rock image since rock particles local background is often different from surrounding particle regions. Object markers are then extracted using the compactness of objects and adaptive morphological reconstruction. The choice of the feature compactness is motivated by the fact that crushed rocks tend to have rounded shapes. Experimental results after comparing the segmented images show that the performance of our algorithm is superior to most standard methods of watershed segmentation. We also show that the proposed algorithm was more robust in the estimation of fines in rock samples than the traditional methods.","PeriodicalId":128378,"journal":{"name":"2010 IEEE 39th Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":"{\"title\":\"Rock image segmentation using watershed with shape markers\",\"authors\":\"A. Amankwah, C. Aldrich\",\"doi\":\"10.1109/AIPR.2010.5759719\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose a method for the creation of object markers used in watershed segmentation of rock images. First, we use adaptive thresholding to segment the rock image since rock particles local background is often different from surrounding particle regions. Object markers are then extracted using the compactness of objects and adaptive morphological reconstruction. The choice of the feature compactness is motivated by the fact that crushed rocks tend to have rounded shapes. Experimental results after comparing the segmented images show that the performance of our algorithm is superior to most standard methods of watershed segmentation. We also show that the proposed algorithm was more robust in the estimation of fines in rock samples than the traditional methods.\",\"PeriodicalId\":128378,\"journal\":{\"name\":\"2010 IEEE 39th Applied Imagery Pattern Recognition Workshop (AIPR)\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"15\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 IEEE 39th Applied Imagery Pattern Recognition Workshop (AIPR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AIPR.2010.5759719\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE 39th Applied Imagery Pattern Recognition Workshop (AIPR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AIPR.2010.5759719","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Rock image segmentation using watershed with shape markers
We propose a method for the creation of object markers used in watershed segmentation of rock images. First, we use adaptive thresholding to segment the rock image since rock particles local background is often different from surrounding particle regions. Object markers are then extracted using the compactness of objects and adaptive morphological reconstruction. The choice of the feature compactness is motivated by the fact that crushed rocks tend to have rounded shapes. Experimental results after comparing the segmented images show that the performance of our algorithm is superior to most standard methods of watershed segmentation. We also show that the proposed algorithm was more robust in the estimation of fines in rock samples than the traditional methods.