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Investigation on multisets mixture learning based object detection
Liu, Zhi-Yong1; Qiao, Hong1; Xu, Lei2
2007-12-01
发表期刊INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
卷号21期号:8页码:1339-1351
文章类型Article
摘要By minimizing the mean square reconstruction error, multisets mixture learning (MML)provides a general approach for object detection in image. To calculate each sample reconstruction error, as the object template is represented by a set of contour points, the MML needs to inefficiently enumerate the distances between the sample and all the contour points. In this paper, we develop the line segment approximation (LSA) algorithm to calculate the reconstruction error, which is shown theoretically and experimentally to be more efficient than the enumeration method. It is also experimentally illustrated that the MML based algorithm has a better noise resistance ability than the generalized Hough transform (GHT) based counterpart.
关键词Object Detection Rival Penalized Competitive Learning Multisets Mixture Learning
WOS标题词Science & Technology ; Technology
关键词[WOS]HOUGH TRANSFORM RHT ; CURVE DETECTION
收录类别SCI ; ISTP
语种英语
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000253298200008
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/9473
专题09年以前成果
通讯作者Liu, Zhi-Yong
作者单位1.Chinese Acad Sci, Lab Complex Syst & Sci, Inst Automat, Beijing 100080, Peoples R China
2.Chinese Univ Hong Kong, Dept Comp Sci & Engn, Shatin, Hong Kong, Peoples R China
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GB/T 7714
Liu, Zhi-Yong,Qiao, Hong,Xu, Lei. Investigation on multisets mixture learning based object detection[J]. INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE,2007,21(8):1339-1351.
APA Liu, Zhi-Yong,Qiao, Hong,&Xu, Lei.(2007).Investigation on multisets mixture learning based object detection.INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE,21(8),1339-1351.
MLA Liu, Zhi-Yong,et al."Investigation on multisets mixture learning based object detection".INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 21.8(2007):1339-1351.
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