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基于轮廓几何稀疏表示的刚性目标模型及其分级检测算法

林煜东 和红杰 陈帆 尹忠科

林煜东, 和红杰, 陈帆, 尹忠科. 基于轮廓几何稀疏表示的刚性目标模型及其分级检测算法. 自动化学报, 2015, 41(4): 843-853. doi: 10.16383/j.aas.2015.c130431
引用本文: 林煜东, 和红杰, 陈帆, 尹忠科. 基于轮廓几何稀疏表示的刚性目标模型及其分级检测算法. 自动化学报, 2015, 41(4): 843-853. doi: 10.16383/j.aas.2015.c130431
LIN Yu-Dong, HE Hong-Jie, CHEN Fan, YIN Zhong-Ke. A Rigid Object Detection Model Based on Geometric Sparse Representation of Profile and Its Hierarchical Detection Algorithm. ACTA AUTOMATICA SINICA, 2015, 41(4): 843-853. doi: 10.16383/j.aas.2015.c130431
Citation: LIN Yu-Dong, HE Hong-Jie, CHEN Fan, YIN Zhong-Ke. A Rigid Object Detection Model Based on Geometric Sparse Representation of Profile and Its Hierarchical Detection Algorithm. ACTA AUTOMATICA SINICA, 2015, 41(4): 843-853. doi: 10.16383/j.aas.2015.c130431

基于轮廓几何稀疏表示的刚性目标模型及其分级检测算法

doi: 10.16383/j.aas.2015.c130431
基金项目: 

国家自然科学基金(61461047)资助

详细信息
    作者简介:

    林煜东 西南交通大学信息科学与技术学院博士研究生.2006年获得华南农业大学信息学院学士学位.2009年获得华南农业大学信息学院硕士学位.主要研究方向为图像表示与图像目标检测.E-mail:willianlam@126.com

    通讯作者:

    陈帆 博士,西南交通大学信息科学与技术学院副教授.主要研究方向为多媒体数据安全,图像处理,计算机应用技术.本文通信作者.E-mail:fchen@home.swjtu.edu.cn

A Rigid Object Detection Model Based on Geometric Sparse Representation of Profile and Its Hierarchical Detection Algorithm

Funds: 

Supported by National Natural Science Foundation of China(61461047)

  • 摘要: 刚性目标轮廓具有明显几何特性且不易受光照、纹理和颜色等因素影响.结合上述特性和图像稀疏表示原理,提出一种适用于刚性目标的分级检测算法.在基于部件模型(Part-based model, PBM)的框架下,采用匹配追踪算法将目标轮廓自适应地稀疏表示为几何部件的组合,根据部件与目标轮廓的匹配度,构建描述部件空间关系的有序链式结构.利用该链式结构的有序特性逐级缩小待检测范围,以匹配度为权值对各级部件显著图进行加权融合生成目标显著图. PASCAL图像库上的检测结果表明,该检测方法对具有显著轮廓特征的刚性目标有较好的检测结果,检测时耗较现有算法减少约60%~90%.
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出版历程
  • 收稿日期:  2013-05-22
  • 修回日期:  2014-11-15
  • 刊出日期:  2015-04-20

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