The feature lines extraction technology of 3D models has been well applied to cultural relics protection and archaeological drawings. For the problem of drawing archaeological documentations, this paper explores a method based on point clouds of cultural relics, which has good anti-noise ability for sparse clouds. In addition, it will cost less time and space complexity to acquire accurate feature.
{"title":"A Method of Feature Lines Generation of Cultural Relics Based on Point Clouds","authors":"Zepeng Wang, Yiyuan Zu, Yixin Shen, Xianmiao Wang, Xi Yang, Zhiyi Zhang","doi":"10.1109/NICOInt.2019.00037","DOIUrl":"https://doi.org/10.1109/NICOInt.2019.00037","url":null,"abstract":"The feature lines extraction technology of 3D models has been well applied to cultural relics protection and archaeological drawings. For the problem of drawing archaeological documentations, this paper explores a method based on point clouds of cultural relics, which has good anti-noise ability for sparse clouds. In addition, it will cost less time and space complexity to acquire accurate feature.","PeriodicalId":436332,"journal":{"name":"2019 Nicograph International (NicoInt)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131998054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-01DOI: 10.1109/NICOInt.2019.00009
Haitian Sun, Kenji Hanata, Hideomi Sato, Ichiro Tsuchitani, T. Akashi
The arrangement of products on store shelves can refer to commercial contracts, sale achievement, and customer satisfaction. At present, clerks check the arrangement manually, which spends time, costs human resource significantly, and can disturb shopping customers. Although automatic methods via computer vision (often incorporating machine learning) can solve the issue, the existing methods need single product template images for detection, facing the difficult collection of master images and the frequent upgrade of products. In this paper, we propose to detect products on store shelves by segmenting the shelves horizontally and vertically without template images and machine learning. The horizontal segmentation is based on clapboard detection via casting lateral gradient votes. The vertical segmentation contains the linear region of interest (ROI) optimization and shadow detection by longitudinal gradient grouping. In experiments, we compare our method with the only existing non-template method, and our method outperforms the existing method.
{"title":"Segmentation based Non-learning Product Detection for Product Recognition on Store Shelves","authors":"Haitian Sun, Kenji Hanata, Hideomi Sato, Ichiro Tsuchitani, T. Akashi","doi":"10.1109/NICOInt.2019.00009","DOIUrl":"https://doi.org/10.1109/NICOInt.2019.00009","url":null,"abstract":"The arrangement of products on store shelves can refer to commercial contracts, sale achievement, and customer satisfaction. At present, clerks check the arrangement manually, which spends time, costs human resource significantly, and can disturb shopping customers. Although automatic methods via computer vision (often incorporating machine learning) can solve the issue, the existing methods need single product template images for detection, facing the difficult collection of master images and the frequent upgrade of products. In this paper, we propose to detect products on store shelves by segmenting the shelves horizontally and vertically without template images and machine learning. The horizontal segmentation is based on clapboard detection via casting lateral gradient votes. The vertical segmentation contains the linear region of interest (ROI) optimization and shadow detection by longitudinal gradient grouping. In experiments, we compare our method with the only existing non-template method, and our method outperforms the existing method.","PeriodicalId":436332,"journal":{"name":"2019 Nicograph International (NicoInt)","volume":"317 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124290135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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