{"title":"基于改进方法的ORB特征提取","authors":"Bingshu Yang, Zhiqiang Wang, Xuejun Yu","doi":"10.1145/3387168.3387169","DOIUrl":null,"url":null,"abstract":"Improvement of the extraction means of the ORB (Oriented FAST and Rotated BRIEF) feature primarily includes optimization concerning excessive aggregation of ORB features and the improvement of the problem that the correct features could not be extracted when regional image illumination is too bright. First, the local self-adaptive threshold was calculated on the basis of the threshold and FAST features were extracted based on the local threshold as the candidate feature points. Then, image iteration was divided into disparate regions and the optimal feature points of the local region were selected as the extraction result. The experimental data showed that the aggregation level of the improved ORB feature lowered more obviously than that of the ORB feature, which adapted to local illumination and the threshold value with high stability; however, the time still met real-time demands.","PeriodicalId":346739,"journal":{"name":"Proceedings of the 3rd International Conference on Vision, Image and Signal Processing","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Extraction of ORB Features with an Improved Method\",\"authors\":\"Bingshu Yang, Zhiqiang Wang, Xuejun Yu\",\"doi\":\"10.1145/3387168.3387169\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Improvement of the extraction means of the ORB (Oriented FAST and Rotated BRIEF) feature primarily includes optimization concerning excessive aggregation of ORB features and the improvement of the problem that the correct features could not be extracted when regional image illumination is too bright. First, the local self-adaptive threshold was calculated on the basis of the threshold and FAST features were extracted based on the local threshold as the candidate feature points. Then, image iteration was divided into disparate regions and the optimal feature points of the local region were selected as the extraction result. The experimental data showed that the aggregation level of the improved ORB feature lowered more obviously than that of the ORB feature, which adapted to local illumination and the threshold value with high stability; however, the time still met real-time demands.\",\"PeriodicalId\":346739,\"journal\":{\"name\":\"Proceedings of the 3rd International Conference on Vision, Image and Signal Processing\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-08-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 3rd International Conference on Vision, Image and Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3387168.3387169\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 3rd International Conference on Vision, Image and Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3387168.3387169","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
摘要
ORB (Oriented FAST and rotating BRIEF)特征提取方法的改进主要包括ORB特征过度聚集的优化和区域图像光照过亮时无法提取正确特征的问题的改进。首先,在阈值的基础上计算局部自适应阈值,并根据该阈值提取FAST特征作为候选特征点;然后,将图像迭代划分为不同的区域,选取局部区域的最优特征点作为提取结果;实验数据表明,改进后的ORB特征的聚集水平比ORB特征降低得更明显,适应局部光照和阈值,稳定性高;然而,时间仍然满足实时需求。
Extraction of ORB Features with an Improved Method
Improvement of the extraction means of the ORB (Oriented FAST and Rotated BRIEF) feature primarily includes optimization concerning excessive aggregation of ORB features and the improvement of the problem that the correct features could not be extracted when regional image illumination is too bright. First, the local self-adaptive threshold was calculated on the basis of the threshold and FAST features were extracted based on the local threshold as the candidate feature points. Then, image iteration was divided into disparate regions and the optimal feature points of the local region were selected as the extraction result. The experimental data showed that the aggregation level of the improved ORB feature lowered more obviously than that of the ORB feature, which adapted to local illumination and the threshold value with high stability; however, the time still met real-time demands.