Wassim Al-Khawand, S. Kadry, Riccardo Bozzo, K. Smaili
{"title":"Accurate, swift and noiseless image binarization","authors":"Wassim Al-Khawand, S. Kadry, Riccardo Bozzo, K. Smaili","doi":"10.1109/ICCVIA.2015.7351908","DOIUrl":null,"url":null,"abstract":"In this paper, we will present an accurate, swift and noiseless image binarization technique that was tested on real life back side container images. Our approach consists of converting a colored image to a grayscaled one, then to construct the histogram which will be divided into group of colors and after that, the foreground - that can be darker or lighter than the background - will be automatically identified and finally, the foreground boundaries will be assiduously enhanced before creating the binarized image. The average processing time of our proposed method is around 0.8 milliseconds which makes it highly suitable for real life multi-user applications.","PeriodicalId":419122,"journal":{"name":"International Conference on Computer Vision and Image Analysis Applications","volume":"365 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Computer Vision and Image Analysis Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCVIA.2015.7351908","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
In this paper, we will present an accurate, swift and noiseless image binarization technique that was tested on real life back side container images. Our approach consists of converting a colored image to a grayscaled one, then to construct the histogram which will be divided into group of colors and after that, the foreground - that can be darker or lighter than the background - will be automatically identified and finally, the foreground boundaries will be assiduously enhanced before creating the binarized image. The average processing time of our proposed method is around 0.8 milliseconds which makes it highly suitable for real life multi-user applications.
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