Each sensor in a multi-sensor visual measurement system has its own local coordinate system to which its measurements are referred. To use the measurement results, the data need to be transformed into a global coordination system. This indicates that all of the local sensor systems relative to the global coordinate system need to be calibrated by a global calibration method. In this paper, a method for calibrating the coordinate systems of a large-scale multi-sensor visual measurement system is introduced. The method utilizes a theodolite coordinate measurement system based on a theodolite pair to realize the global calibration. During the global calibration process, the theodolite coordinate measurement system measures the 3D coordinates of the feature points within each visual sensor relative to a global coordinate system. Then the global calibration can be implemented by means of direct and indirect transformation methods depending on the calibration targets. The calibration method has been successfully applied in practical applications.

This paper reviews the recent progress in surface measurement methods using active vision and light-scattering techniques. The active vision methods with different structured light patterns and the corresponding techniques are summarized. The surface roughness and defects inspection with light-scattering are discussed. After the review, an integrative method to measure surface waviness and form, roughness is proposed.

In this paper, on-line and real-time laser visual alignment measurement technique is studied to measure the spatial straightness of large objects. The basic principle of the visual alignment measurement is detailed. The mathematics models of visual sensor, measurement system and their calibrations are presented. Based on the technique, an experiment system is developed and the straightness of a 1500 mm long seamless steel pipe is inspected. The experiment results show that the measurement technique can achieve total on-line measurement and it offers high precision and stability, which is of practical importance for the application.

Each sensor in a large-scale machine vision system has its own local coordinate system to which its measurements are referred. To use the measurement results, the data need to be transformed into a global coordination system's. This dictates that all of the local sensor systems relative to the global coordinate system need to be calibrated by a global calibration method. In this paper, a global calibration method for determining the relationship between each sensor coordinate system and the global coordinate system in a large-scale machine vision system is introduced. Two coordinate transformation methods corresponding to the global calibration method, direct and indirect transformation methods, are developed. The global calibration method is very useful and has been successfully used in many practical applications, and very good results have been achieved, as observed from the experimental results in the end of the paper.

Uncalibrated reconstruction of a scene is desired in many practical applications of computer vision. However, using a single camera with unconstrained motion and unknown parameters, a true Euclidean three-dimensional (3-D) model of the scene cannot be reconstructed. In this paper, we present a method for true Euclidean 3-D reconstruction using an active vision system consisting of a pattern projector and a camera. When the intrinsic and extrinsic parameters of the camera are changed during the reconstruction, they can be self-calibrated and the real 3-D model of the scene can then be reconstructed. The parameters of the projector are precalibrated and are kept constant during the reconstruction process. This allows the configuration of the vision system to be varied during a reconstruction task, which increases its selfadaptability to the environment or scene structure in which it is to work.