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    제목(영문) Neural- NeTwork Model for Compensation of Lens Distortion in Camera Calibration
    저자 (Byeong-Mook Chung ,School of mechanical engineering, Yeungnam University, Gyongsan, South Korea ) ▷공저자네트워크등록하기
    초록
    초록(영문)

    Camera calibration for machine vision is critical in three-dimensional (3-D) measurement systems based on a digital light processing
    (DLP) projector and a camera. The Z-height of the measurement point is calculated using the phase value observed by the camera
    when a fringe pattern is scanned from a projection onto an object. On the other hand, the X and Y coordinates are obtained from
    the camera coordinates using a transformation matrix, and the mathematical model for lens distortion is additionally used However,
    the errors for x and y coordinates are 10 times larger than the z-height error in an experiment. This is because the lens distortion
    is not sufficiently compensated in the mathematical model considering only the position from the lens center. Therefore, the neural
    network (NN). model that considers the measurement distance in addition to the position is proposed in this paper. Experiments were
    conducted on a 100 x JOO mm2 area, and a maximum error of 0.5 mm is observed for the mathematical model. However, when the
    NN model considering the height of the object is used, the error is reduced by 60% to 0.2 mm.

    keyword Camera calibration, Lens distortion, 30 measurement, Neural network, Transformation matrix
    저널명 International Journal of Precision Engineering and Manufacturing ▷관련저널보기
    VOL 19
    PAGE 0959
    발표년도 2018
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