{"title":"植物表型中的计算机视觉问题,CVPPP 2017: CVPPP 2017研讨会论文介绍","authors":"H. Scharr, T. Pridmore, S. Tsaftaris","doi":"10.1109/ICCVW.2017.236","DOIUrl":null,"url":null,"abstract":"Plant phenotyping is the identification of effects on the phenotype (i.e., the plant appearance and behavior) as a result of genotype differences (i.e., differences in the genetic code) and the environment. Previously, the process of taking phenotypic measurements has been laborious, costly, and time consuming. In recent years, non-invasive, image-based methods have become more common. These images are recorded by a range of capture devices from small embedded camera systems to multi-million Euro smart-greenhouses, at scales ranging from microscopic images of cells, to entire fields captured by UAV imaging. These images needs to be analyzed in a high throughput, robust, and accurate manner.","PeriodicalId":149766,"journal":{"name":"2017 IEEE International Conference on Computer Vision Workshops (ICCVW)","volume":"474 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"17","resultStr":"{\"title\":\"Computer Vision Problems in Plant Phenotyping, CVPPP 2017: Introduction to the CVPPP 2017 Workshop Papers\",\"authors\":\"H. Scharr, T. Pridmore, S. Tsaftaris\",\"doi\":\"10.1109/ICCVW.2017.236\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Plant phenotyping is the identification of effects on the phenotype (i.e., the plant appearance and behavior) as a result of genotype differences (i.e., differences in the genetic code) and the environment. Previously, the process of taking phenotypic measurements has been laborious, costly, and time consuming. In recent years, non-invasive, image-based methods have become more common. These images are recorded by a range of capture devices from small embedded camera systems to multi-million Euro smart-greenhouses, at scales ranging from microscopic images of cells, to entire fields captured by UAV imaging. These images needs to be analyzed in a high throughput, robust, and accurate manner.\",\"PeriodicalId\":149766,\"journal\":{\"name\":\"2017 IEEE International Conference on Computer Vision Workshops (ICCVW)\",\"volume\":\"474 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"17\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE International Conference on Computer Vision Workshops (ICCVW)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCVW.2017.236\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE International Conference on Computer Vision Workshops (ICCVW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCVW.2017.236","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Computer Vision Problems in Plant Phenotyping, CVPPP 2017: Introduction to the CVPPP 2017 Workshop Papers
Plant phenotyping is the identification of effects on the phenotype (i.e., the plant appearance and behavior) as a result of genotype differences (i.e., differences in the genetic code) and the environment. Previously, the process of taking phenotypic measurements has been laborious, costly, and time consuming. In recent years, non-invasive, image-based methods have become more common. These images are recorded by a range of capture devices from small embedded camera systems to multi-million Euro smart-greenhouses, at scales ranging from microscopic images of cells, to entire fields captured by UAV imaging. These images needs to be analyzed in a high throughput, robust, and accurate manner.