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Abstract. Robotic eye-in-hand configuration is very useful in many applications. Essential in the use of this configuration is robotic hand-eye calibration. The method proposed in this paper, calibrating the optical axis of the camera and a target pattern, creates an environment for camera calibration without using additional equipment. By using the geometric relation between the optical axis and the robot hand, calibration of the optical axis and the target pattern becomes a feasible process. The proposed method requires identification of the points intersected by the optical axis with the target pattern. Image-processirig techniques for identifying these intersection points are developed and implemented by designing the target pattern with a checkerboard pattern. The accuracy issue of the proposed method is discussed. Finally, experimental results are presented to verify the proposed method.

Using vision sensors to study object deformation is introduced in this article. The vision sensor is used to identify the deformation characteristic and estimate the deformation behavior of flexible beams. First, a nonlinear equation describing the deformation of the beam is given. Then, cubic spline functions are used to approximate the contour of the deformed beam after the beam is detected by the vision sensor. Using the cubic spline approximation and Maclaurin's series expansion, a procedure to determine the deformation characteristic is formulated. To estimate the deformation behavior, numerical differential method to solve the nonlinear equation is used. The error of this numerical method can be controlled with the aid of the vision sensor. Finally, experimental results are given to verify that the proposed methods are effective.

Abstract-Passive stereo vision is proposed to work in a complimentary manner with active stereo vision for detection of smooth surfaces of deformed plates. Passive stereo vision is used to obtain the boundary of the smooth surfaces, whereas active stereo vision with the projection of structured light is applied to detect the details of the surfaces. An inherent problem in passive stereo vision, called false boundary problem, is identified. The problem is solved by calibrating the structured light in active stereo vision and dynamically placing the cameras in passive stereo vision. The matching criteria in active stereo vision and the sensing process of the proposed approach are presented. An experiment was conducted to test the effectiveness of the proposed approach.