{"title":"农业用图像注释工具。需求研究","authors":"Marcin Bator, Maciej Pankiewicz","doi":"10.22630/mgv.2019.28.1.7","DOIUrl":null,"url":null,"abstract":"Images of natural scenes, like those relevant for agriculture, are characterised with a variety of forms of objects of interest and similarities between objects that one might want to discriminate. This introduces uncertainty to the analysis of such images. Requirements for an image annotation tool to be used in pattern recognition design for agriculture were discussed. A selection of open source annotating tools were presented. Advices how to use the software to handle uncertainty and missing functionalities were described.","PeriodicalId":39750,"journal":{"name":"Machine Graphics and Vision","volume":"24 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Image annotating tools for agricultural purpose - A requirements study\",\"authors\":\"Marcin Bator, Maciej Pankiewicz\",\"doi\":\"10.22630/mgv.2019.28.1.7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Images of natural scenes, like those relevant for agriculture, are characterised with a variety of forms of objects of interest and similarities between objects that one might want to discriminate. This introduces uncertainty to the analysis of such images. Requirements for an image annotation tool to be used in pattern recognition design for agriculture were discussed. A selection of open source annotating tools were presented. Advices how to use the software to handle uncertainty and missing functionalities were described.\",\"PeriodicalId\":39750,\"journal\":{\"name\":\"Machine Graphics and Vision\",\"volume\":\"24 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Machine Graphics and Vision\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.22630/mgv.2019.28.1.7\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Machine Graphics and Vision","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.22630/mgv.2019.28.1.7","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Image annotating tools for agricultural purpose - A requirements study
Images of natural scenes, like those relevant for agriculture, are characterised with a variety of forms of objects of interest and similarities between objects that one might want to discriminate. This introduces uncertainty to the analysis of such images. Requirements for an image annotation tool to be used in pattern recognition design for agriculture were discussed. A selection of open source annotating tools were presented. Advices how to use the software to handle uncertainty and missing functionalities were described.
期刊介绍：
Machine GRAPHICS & VISION (MGV) is a refereed international journal, published quarterly, providing a scientific exchange forum and an authoritative source of information in the field of, in general, pictorial information exchange between computers and their environment, including applications of visual and graphical computer systems. The journal concentrates on theoretical and computational models underlying computer generated, analysed, or otherwise processed imagery, in particular: - image processing - scene analysis, modeling, and understanding - machine vision - pattern matching and pattern recognition - image synthesis, including three-dimensional imaging and solid modeling
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
