• DocumentCode
    2777004
  • Title

    Vergence Control of 2 DOF Pan-Tilt Binocular Cameras using a Log-Polar Representation of the Visual Cortex

  • Author

    Zhang, A.X.J. ; Tay, Alex Leng Phuan

  • Author_Institution
    Nanyang Technol. Univ., Singapore
  • fYear
    0
  • fDate
    0-0 0
  • Firstpage
    4277
  • Lastpage
    4283
  • Abstract
    This paper presents a neurologically inspired vergence control model that uses the optimization of the disparity error between interlaced cortical maps incident on the visual cortex (VC). This was inspired by the work of Hubel et al [4] where their investigations led to the discovery of the alternating organization of ocular dominance columns. While the implemented system consists of many modules including a simple component to function as the superior colliculus (SC) and modeling of attention traces from the Frontal Eye Fields (FEF) [5], this paper emphasizes the parts specific to using the log-polar maps on the visual cortex for vergence control. We explain how the log-polar image incident on the VC can be used successfully to determine the pan angle (saccade) to perform binocular vergence on points of in the scene. A discussion at the end of the paper highlights the significant differences between this system and the conventional object correspondence systems that require matching of specific object shapes. In this model, the cortically magnified VC image is used to match the entire image.
  • Keywords
    image matching; image representation; image sensors; optimisation; robot vision; 2 DOF pan-tilt binocular cameras; cortically magnified VC image; disparity error optimization; image matching; interlaced cortical maps; log-polar image incident; log-polar representation; neurologically inspired vergence control; visual cortex; Brain modeling; Cameras; Control systems; Data mining; Error correction; Layout; Machine vision; Muscles; Shape; Virtual colonoscopy; Binocular Vergence; Log-Polar Transform; Superior Colliculus; Visual Cortex;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2006. IJCNN '06. International Joint Conference on
  • Conference_Location
    Vancouver, BC
  • Print_ISBN
    0-7803-9490-9
  • Type

    conf

  • DOI
    10.1109/IJCNN.2006.247001
  • Filename
    1716690