提高零售产品的识别度:细粒度的瓶子尺寸分类

Katarina Tolja, M. Subašić, Z. Kalafatić, S. Lončarić
{"title":"提高零售产品的识别度:细粒度的瓶子尺寸分类","authors":"Katarina Tolja, M. Subašić, Z. Kalafatić, S. Lončarić","doi":"10.23919/MVA57639.2023.10215699","DOIUrl":null,"url":null,"abstract":"In this paper, we propose two innovative approaches to tackle the key challenges in product size classification, with a specific focus on bottles. Our research is particularly interesting as we leverage the bottle cap as a reference object, which allows bottle size classification to overcome challenges in the distance between the capturing device and the retail shelf, viewing angle, and arrangement of bottles on the shelves. We showcase the usage of the reference object in explicit and implicit novel approaches and discuss the benefits and limitations of the proposed methods.","PeriodicalId":338734,"journal":{"name":"2023 18th International Conference on Machine Vision and Applications (MVA)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Enhancing Retail Product Recognition: Fine-Grained Bottle Size Classification\",\"authors\":\"Katarina Tolja, M. Subašić, Z. Kalafatić, S. Lončarić\",\"doi\":\"10.23919/MVA57639.2023.10215699\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose two innovative approaches to tackle the key challenges in product size classification, with a specific focus on bottles. Our research is particularly interesting as we leverage the bottle cap as a reference object, which allows bottle size classification to overcome challenges in the distance between the capturing device and the retail shelf, viewing angle, and arrangement of bottles on the shelves. We showcase the usage of the reference object in explicit and implicit novel approaches and discuss the benefits and limitations of the proposed methods.\",\"PeriodicalId\":338734,\"journal\":{\"name\":\"2023 18th International Conference on Machine Vision and Applications (MVA)\",\"volume\":\"53 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-07-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 18th International Conference on Machine Vision and Applications (MVA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/MVA57639.2023.10215699\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 18th International Conference on Machine Vision and Applications (MVA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/MVA57639.2023.10215699","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

在本文中,我们提出了两种创新的方法来解决产品尺寸分类的关键挑战,特别关注瓶子。我们的研究特别有趣,因为我们利用瓶盖作为参考对象,这使得瓶子尺寸分类能够克服捕获设备与零售货架之间的距离、视角和货架上瓶子的排列等挑战。我们展示了参考对象在显式和隐式新方法中的使用,并讨论了所提出方法的优点和局限性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing Retail Product Recognition: Fine-Grained Bottle Size Classification
In this paper, we propose two innovative approaches to tackle the key challenges in product size classification, with a specific focus on bottles. Our research is particularly interesting as we leverage the bottle cap as a reference object, which allows bottle size classification to overcome challenges in the distance between the capturing device and the retail shelf, viewing angle, and arrangement of bottles on the shelves. We showcase the usage of the reference object in explicit and implicit novel approaches and discuss the benefits and limitations of the proposed methods.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信