• DocumentCode
    2208145
  • Title

    Exploiting automatic image segmentation to human detection and depth estimation

  • Author

    Tseng, Hsiao-Chun ; Shyu, Jia-Jye ; Chang, Jyh-Yeong ; Lin, Chin-Teng

  • Author_Institution
    Dept. of Electr. Eng., Nat. Chiao Tung Univ., Hsinchu, Taiwan
  • fYear
    2011
  • fDate
    11-15 April 2011
  • Firstpage
    19
  • Lastpage
    25
  • Abstract
    In this paper, we combine image segmentation techniques and face detection methods to extract the human from scenes. Firstly, skin regions are detected and an ellipse fitting method is employed to detect the face region and consequently locate the human position. Then we propose an improved automatic seeded region growing algorithm to segment the image. The initial seeds are generated automatically, and the remaining pixels are classified to the nearest region. After the region growing procedure, two neighboring regions with high similarity are merged. The human body is determined by confining semantic human body region in segmented regions, and those belonging to the human face and human body are merged afterward. Lastly, we will detect the human vertical y-coordinate values in the image, and the depths can then be estimated according to the depth look-up tables of the camera.
  • Keywords
    face recognition; feature extraction; image segmentation; object detection; table lookup; automatic image segmentation techniques; automatic seeded region growing algorithm; depth estimation; depth look-up tables; ellipse fitting method; face detection methods; human detection; skin region detection; vertical y-coordinate values; Cameras; Estimation; Image segmentation; Pixel; Human Depth Estimation; Human Detection; Region Growing; Skin Detection;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence for Multimedia, Signal and Vision Processing (CIMSIVP), 2011 IEEE Symposium on
  • Conference_Location
    Paris
  • Print_ISBN
    978-1-4244-9913-7
  • Type

    conf

  • DOI
    10.1109/CIMSIVP.2011.5949245
  • Filename
    5949245