{"title":"Segmentation algoritm for touching round grain image","authors":"K. Kiratiratanapruk, W. Sinthupinyo","doi":"10.1109/ICEIE.2010.5559878","DOIUrl":null,"url":null,"abstract":"In this paper, we propose touching round grain segmentation technique based on center of individual grain and concavity of image boundary. The objective of this work is to identify single grain and detect position of touching round grain in binary image. First, center of grain is detected by using conventional technique such as Morphological algorithm and Color component labeling. From the detected centers of grain, we can separate individual grains from clustered grains. The clustered grains are further processed to detect concave points among centers of grain. We then apply several criteria such as distance, the opposite orientation and the angle to each others to determine splitting paths. The experimental results show good performance in separating touching round grains are of benefit for intelligent grain analysis.","PeriodicalId":211301,"journal":{"name":"2010 International Conference on Electronics and Information Engineering","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2010-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 International Conference on Electronics and Information Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEIE.2010.5559878","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
In this paper, we propose touching round grain segmentation technique based on center of individual grain and concavity of image boundary. The objective of this work is to identify single grain and detect position of touching round grain in binary image. First, center of grain is detected by using conventional technique such as Morphological algorithm and Color component labeling. From the detected centers of grain, we can separate individual grains from clustered grains. The clustered grains are further processed to detect concave points among centers of grain. We then apply several criteria such as distance, the opposite orientation and the angle to each others to determine splitting paths. The experimental results show good performance in separating touching round grains are of benefit for intelligent grain analysis.