期刊文献+

基于运动谱残差的视频显著性检测算法 被引量:4

Video Saliency Detection Algorithm Based on Motion Spectral Residual
下载PDF
导出
摘要 借鉴静态图像中通过计算像素灰度的谱残差检测显著性区域的方法,提出基于运动谱残差的视频显著性检测算法。通过提取每一帧的运动矢量场,对矢量场的水平和垂直分量分别计算运动谱残差,融合2个方向的运动谱残差形成运动谱残差图,将谱残差图进行顶帽变换形成视频显著图。实验结果表明,该算法能准确地分割出各种运动场景的显著性区域,在检测效果和抗噪能力方面优于现有视频显著性检测算法,适用于运动微生物检测、行人车辆检测等领域。 This paper references the idea of detecting salient area in still image through calculating spectral residual of the gray-level pixel value,so proposes a video saliency detection algorithm based on motion spectral residual. It extracts each frame’ s motion vector field,calculates motion spectral residual of the horizontal and vertical components of motion vector field,merges the two components’ motion spectral residual to form a motion spectral residual image,and finally exertes a top-hat transformation on the motion spectral residual image to form video saliency image. Experimental results show that this algorithm can accurately segment saliency region out of various motion scenes,outperform existing video saliency detection algorithms in terms of detection accuracy and noise resistance. The algorithm can be applied to such field as moving microbe detection,pedestrian detection and vehicle detection.
出处 《计算机工程》 CAS CSCD 2014年第12期247-250,257,共5页 Computer Engineering
基金 国家自然科学基金资助项目(40927001)
关键词 视频显著性 谱残差 光流法 运动矢量 顶帽变换 图像增强 video saliency spectral residual optical flow method motion vector top-hat transformation image enhancement
  • 相关文献

参考文献13

  • 1Itti L,Koch C,Niebur E.A Model of Saliency-based Visual Attention for Rapid Scene Analysis[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1998,20(11):1254-1259.
  • 2Harel J,Koch C,Perona P.Graph-based Visual Saliency[C]//Proceedings of NIPS’06.[S.l.]:IEEE Press,2006:545-552.
  • 3Hou Xiaodi,Zhang Liqing.Saliency Detection:A Spectral Residual Approach[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Minneapolis,USA:IEEE Press,2007:1-7.
  • 4Cheng Mingming,Zhang Guoxin,Mitra N J,et al.Global Contrast Based Salient Region Detection[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.[S.l.]:Springer,2011:409-416.
  • 5Perazzi F,Krahenbuhl P,Pritch Y,et al.Saliency Filters:Contrast Based Filtering for Salient Region Detection[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Providence,USA:IEEE Press,2012:733-740.
  • 6Yang Chuan,Zhang Lihe,Lu Huchuan,et al.Saliency Detection via Graph-based Manifold Ranking[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Providence,USA:IEEE Press,2012:3166-3173.
  • 7Zivkovic Z.Improved Adaptive Gaussian Mixture Model for Background Subtraction[C]//Proceedings of IEEE Conference on Pattern Recognition.Cambridge,UK:IEEE Press,2004:28-31.
  • 8Zhai Yun,Shah M.Visual Attention Detection in Video Sequences Using Spatiotemporal Cues[C]//Proceedings of the14th Annual ACM International Conference on Multimedia.New York,USA:ACM Press,2006:815-824.
  • 9Guo Chenlei,Ma Qi,Zhang Liming.Spatio-temporal Saliency Detection Using Phase Spectrum of Quaternion Fourier Transform[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.[S.l.]:IEEE Press,2008:1-8.
  • 10Cui Xinyi,Liu Qingshan,Metaxas D.Temporal Spectral Residual:Fast Motion Saliency Detection[C]//Proceedings of20131EEE Conference on Computer Vision and Pattern Recognition.[S.l.]:IEEE Press,2009:617-620.

二级参考文献11

共引文献5

同被引文献25

  • 1张鹏,王润生.基于视点转移和视区追踪的图像显著区域检测[J].软件学报,2004,15(6):891-898. 被引量:53
  • 2Borji A,Sihite D N, Itti L. Salient Object Detection: A Benchmark [ C]//Proceedings of the 12th European Conference on Computer Vision. Berlin, Germany: Springer-Verlag, 2012 : 414-429.
  • 3Karssemeijer N, Brake G M. Detection of Stellate Distor- tions in Mammograms [ J ]. IEEE Transactions on Medical Imaging ,2006,15 (5) :611-619.
  • 4Zhang Wei,Wu Q M J,Wang Guanghui,et al. An Adaptive Computational Model for Salient Object Detection [ J ]. IEEE Transactions on Multimedia,2010,12(4) :300-316.
  • 5Meur O L, Callet P L, Barba D, et al. A Coherent Computational Approach to Model Bottom-up Visual Attention [ J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006,28 ( 5 ) : 802-817.
  • 6Wu Ying. A Unified Approach to Salient Object Detection Via Low Rank Matrix Recovery[ C ]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Washington D. C., USA: IEEE Press,2012:853-860.
  • 7Liu Tie,Yuan Zejian,Sun Jian, et al. Learning to Detect a Salient Object [J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2011,33 ( 2 ) :353-367.
  • 8Chung Yuk-Ying, Wahid N. A Hybrid Network Intrusion Detection System Using Simplified Swarm Optimiza- tion(SSO) [J]. Applied Soft Computing, 2012, 12 ( 9 ) : 3014-3022.
  • 9Jordehi A R,Jasni J. Heuristic Methods for Solution of Facts Optimization Problem in Power Systems [ C ]// Proceedings of IEEE Student Conference on Research and Development. Washington D. C. , USA : IEEE Press, 2011:30-35.
  • 10刘娟妮,彭进业,李大湘,王平.基于谱残差和多分辨率分析的显著目标检测[J].中国图象图形学报,2011,16(2):244-249. 被引量:12

引证文献4

二级引证文献17

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部