• DocumentCode
    2637011
  • Title

    A Figure Extraction and Synthesis System by Learning Vector Quantization Neural Networks

  • Author

    Chang, Chuan-Yu ; Tsai, Zong-Yu ; Li, Chun-Hsi

  • Author_Institution
    Inst. of Comput. Sci. & Inf. Eng., Nat. Yunlin Univ. of Sci. & Technol., Douliou
  • fYear
    2008
  • fDate
    18-20 June 2008
  • Firstpage
    324
  • Lastpage
    324
  • Abstract
    Extracting complete figures from videos with complicated environments is difficult. A new figure extraction and synthesis system with capability of extracting figures from consecutive frames in a messy environment is proposed in this paper. A figure template is constructed based on the face detection results and some image processing techniques. Figural and non-figural features are extracted from the figure images. By means of these features, a learning vector quantization neural network (LVQNN) is applied to classify the uncertain regions into figural and non-figural objects. The extracted figure can be further synthesized into an optional cinestrip. Experimental results showed the proposed method successfully extract the figure object from a complex background environment.
  • Keywords
    feature extraction; image coding; learning (artificial intelligence); neural nets; vector quantisation; face detection; figure extraction; figure synthesis system; image processing techniques; learning vector quantization neural network; Bismuth; Computer science; Data mining; Discrete cosine transforms; Face detection; Image processing; Network synthesis; Neural networks; Vector quantization; Videos;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Innovative Computing Information and Control, 2008. ICICIC '08. 3rd International Conference on
  • Conference_Location
    Dalian, Liaoning
  • Print_ISBN
    978-0-7695-3161-8
  • Electronic_ISBN
    978-0-7695-3161-8
  • Type

    conf

  • DOI
    10.1109/ICICIC.2008.29
  • Filename
    4603513