• DocumentCode
    691113
  • Title

    The Multi-orientation Target Recognition Method Based on Visual Attention

  • Author

    Du Yaling ; Lin Beiqing ; Lu Jing

  • Author_Institution
    Nat. Key Lab. of Sci. & Technol. on Aerosp. Intell. Control, Beijing Aerosp. Autom. Control Inst., Beijing, China
  • fYear
    2013
  • fDate
    21-23 Sept. 2013
  • Firstpage
    776
  • Lastpage
    780
  • Abstract
    Synthetically utilizing image visual attention and Support Vector Machine (SVM) classification method, a multi-orientation target recognition algorithm was proposed to detect multi-orientation targets in images. Firstly, according to human visual system, the saliency image was get rapidly using visual attention to improve the efficiency. Secondly, the Histogram of Oriented Gradients (HOG) features described the shape features of target. Then, the angular field of view to targets was divided into several parts for solving the samples variety according to the pose angle. In every divided field SVM classifier was used to recognize the multi-orientation targets. Experimental results show that the multi-view target recognition method proposed by this paper is effective and reliable.
  • Keywords
    feature extraction; image classification; object detection; object recognition; support vector machines; HOG; SVM classifier; histogram-of-oriented gradients features; human visual system; multiorientation target detection; multiorientation target recognition method; shape features; support vector machine classification method; synthetic image visual attention utilization; Aerospace control; Algorithm design and analysis; Computational modeling; Histograms; Support vector machines; Target recognition; Visualization; Histograms of oriented gradient; Multi-orientation Target recognition; Support Vector Machine; Visual Attention;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Instrumentation, Measurement, Computer, Communication and Control (IMCCC), 2013 Third International Conference on
  • Conference_Location
    Shenyang
  • Type

    conf

  • DOI
    10.1109/IMCCC.2013.173
  • Filename
    6840563