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复杂动态场景下目标检测与分割算法

许冰 牛燕雄 吕建明

许冰, 牛燕雄, 吕建明等 . 复杂动态场景下目标检测与分割算法[J]. 北京航空航天大学学报, 2016, 42(2): 310-317. doi: 10.13700/j.bh.1001-5965.2015.0113
引用本文: 许冰, 牛燕雄, 吕建明等 . 复杂动态场景下目标检测与分割算法[J]. 北京航空航天大学学报, 2016, 42(2): 310-317. doi: 10.13700/j.bh.1001-5965.2015.0113
XU Bing, NIU Yanxiong, LYU Jianminget al. Object detection and segmentation algorithm in complex dynamic scene[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(2): 310-317. doi: 10.13700/j.bh.1001-5965.2015.0113(in Chinese)
Citation: XU Bing, NIU Yanxiong, LYU Jianminget al. Object detection and segmentation algorithm in complex dynamic scene[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(2): 310-317. doi: 10.13700/j.bh.1001-5965.2015.0113(in Chinese)

复杂动态场景下目标检测与分割算法

doi: 10.13700/j.bh.1001-5965.2015.0113
详细信息
    作者简介:

    许冰 女,博士研究生。主要研究方向:目标的检测与识别。Tel.:010-82316906-868 E-mail:xubing@buaa.edu.cn;牛燕雄 男,博士,教授,博士生导师。主要研究方向:光电对抗。Tel.:010-82316906-868 E-mail:niuyx@buaa.edu.cn

    通讯作者:

    牛燕雄,Tel.:010-82316906-868 E-mail:niuyx@buaa.edu.cn

  • 中图分类号: TP391

Object detection and segmentation algorithm in complex dynamic scene

  • 摘要: 在动态场景等复杂条件下,往往难以对序列图像目标进行准确的检测与分割。根据序列图像中目标在复杂条件下的成像特点,提出了一种基于融合尺度不变特征变换(SIFT)流特征显著模型的动态场景目标检测与分割算法。通过对SIFT流算法表示运动特征信息的优势进行分析,并结合图像国际照明协会(CIE)Lab颜色空间的颜色和亮度特征信息,建立四维特征向量空间。利用改进的多尺度中心-环绕对比方法生成各特征通道的显著图并进行线性融合,建立序列图像的动态场景目标显著模型。最后利用均值漂移聚类算法和形态学处理实现对检测目标的精确分割。实验结果表明,相比传统检测与分割算法,该算法在动态背景与航拍等复杂场景下能够分割出更为完整的目标区域,具有良好的鲁棒性和高分割精度。

     

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出版历程
  • 收稿日期:  2015-03-05
  • 网络出版日期:  2016-02-20

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