8th International Conference on Body Area Networks

Research Article

Camera Modeling Technique of 3D Sensing Based on Tile Coding for Computer Vision

  • @INPROCEEDINGS{10.4108/icst.bodynets.2013.253708,
        author={Toshihiko Watanabe and Yuichi Saito},
        title={Camera Modeling Technique of 3D Sensing Based on Tile Coding for Computer Vision},
        proceedings={8th International Conference on Body Area Networks},
        publisher={ICST},
        proceedings_a={BODYNETS},
        year={2013},
        month={10},
        keywords={computer vision stereo vision camera model tile coding cmac perspective projection},
        doi={10.4108/icst.bodynets.2013.253708}
    }
    
  • Toshihiko Watanabe
    Yuichi Saito
    Year: 2013
    Camera Modeling Technique of 3D Sensing Based on Tile Coding for Computer Vision
    BODYNETS
    ACM
    DOI: 10.4108/icst.bodynets.2013.253708
Toshihiko Watanabe1,*, Yuichi Saito1
  • 1: Osaka Electro-Communication University
*Contact email: t-wata@isc.osakac.ac.jp

Abstract

Recently, the 3D sensing technique using multiple cameras has been applied to various areas such as visualization, motion capturing, and so on. However, improvement of the camera model calibration is indispensible for more precise measurement. In this study, we propose a camera modeling technique for 3D sensing based on tile coding (CMAC) structure. A distance between a sensing target and the camera is used to construct the camera model considering optical projection characteristics. In our approach, the least mean square error method is successfully applied considering the simple tile structure to formulate the camera model. Then iterative calculations for solving the inverse problem of the 3-D to 2-D projection by camera are performed to attain measured 3D coordinates. Through sensing experiments of stereo vision measurement based on the proposed approach, we showed the performance of the model was drastically improved compared with the conventional modeling approach such as Open CV model or crisp partitioned model.