彩色图像序列中目标关联与跟踪的两种相似度统计方法

H. L. Kennedy
{"title":"彩色图像序列中目标关联与跟踪的两种相似度统计方法","authors":"H. L. Kennedy","doi":"10.1109/ICIP.2007.4379196","DOIUrl":null,"url":null,"abstract":"Two statistical measures of similarity, for data association and tracking moving objects in sequences of color images, are derived and their performance is compared with normalized cross-correlation. Both methods use an F-distributed test statistic in a hypothesis test, which permits association thresholds to be set to give the desired (theoretical) false-association rate. One of the methods matches the performance of normalized cross-correlation, in the test data used, and is computationally less expensive.","PeriodicalId":131177,"journal":{"name":"2007 IEEE International Conference on Image Processing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2007-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Two Statistical Measures of Similarity for Object Association and Tracking in Color Image Sequences\",\"authors\":\"H. L. Kennedy\",\"doi\":\"10.1109/ICIP.2007.4379196\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Two statistical measures of similarity, for data association and tracking moving objects in sequences of color images, are derived and their performance is compared with normalized cross-correlation. Both methods use an F-distributed test statistic in a hypothesis test, which permits association thresholds to be set to give the desired (theoretical) false-association rate. One of the methods matches the performance of normalized cross-correlation, in the test data used, and is computationally less expensive.\",\"PeriodicalId\":131177,\"journal\":{\"name\":\"2007 IEEE International Conference on Image Processing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-11-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 IEEE International Conference on Image Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIP.2007.4379196\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE International Conference on Image Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIP.2007.4379196","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

导出了用于彩色图像序列中数据关联和运动目标跟踪的两种统计相似性度量,并将其性能与归一化互相关进行了比较。这两种方法都在假设检验中使用f分布检验统计量,这允许设置关联阈值来给出期望的(理论的)假关联率。在使用的测试数据中,其中一种方法与归一化互相关的性能相匹配,并且计算成本更低。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Two Statistical Measures of Similarity for Object Association and Tracking in Color Image Sequences
Two statistical measures of similarity, for data association and tracking moving objects in sequences of color images, are derived and their performance is compared with normalized cross-correlation. Both methods use an F-distributed test statistic in a hypothesis test, which permits association thresholds to be set to give the desired (theoretical) false-association rate. One of the methods matches the performance of normalized cross-correlation, in the test data used, and is computationally less expensive.
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