A new approach to automatic reconstruction of a 3-D world using active stereo vision
Computer Vision and Image Understanding
The CardEye: A Trinocular Active Vision System
ICVS '01 Proceedings of the Second International Workshop on Computer Vision Systems
Free-Hand Pointer by Use of an Active Stereo Vision System
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Toward Automatic Reconstruction of 3D Environment with an Active Binocular Head
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
The Agile Stereo Pair for active vision
Machine Vision and Applications
The model for optimal design of robot vision systems based on kinematic error correction
Image and Vision Computing
Active, Foveated, Uncalibrated Stereovision
International Journal of Computer Vision
Camera-to-camera mapping for hybrid pan-tilt-zoom sensors calibration
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
Real-time visuomotor update of an active binocular head
Autonomous Robots
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In this paper, we show how an active binocular head, the IIS head, can be easily calibrated with very high accuracy. Our calibration method can also be applied to many other binocular heads. In addition to the proposal and demonstration of a four-stage calibration process, there are three major contributions in this paper. First, we propose a motorized-focus lens (MFL) camera model which assumes constant nominal extrinsic parameters. The advantage of having constant extrinsic parameters is to having a simple head/eye relation. Second, a calibration method for the MFL camera model is proposed in this paper, which separates estimation of the image center and effective focal length from estimation of the camera orientation and position. This separation has been proved to be crucial; otherwise, estimates of camera parameters would be very noise-sensitive. Thirdly, we show that, once the parameters of the MFL camera model is calibrated, a nonlinear recursive least-square estimator can be used to refine all the 35 kinematic parameters. Real experiments have shown that the proposed method can achieve accuracy of one pixel prediction error and 0.2 pixel epipolar error, even when all the joints, including the left and right focus motors, are moved simultaneously. This accuracy is good enough for many 3D vision applications, such as navigation, object tracking and reconstruction