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.

In this paper, we describe a calibration method for 3D vision system using pattern projection.The calibration consists of two phases: offline calibration of the parameters of the pattern projector and on-line calibration of the varying intrinsic and extrinsic parameters of the camera. During the on-line recalibration, we do not need to calibrate the homographies of all the light stripe planes relative to the camera, but just need to calibrate those of the two or more arbitrary light planes. Then all of the other light stripe planes homographies relative to the camera image plane can be recovered based on the relation derived in this paper between the homographies of the light stripe planes and their transformations with respect to the world coordinate frame. By this method, we can easily implement recalibration of the 3D vision system with its pattern projection when the intrinsic and extrinsic parameters of the camera are changed.

In this paper, the statistical properties of speckle pattern are investigated from the point of view of computer texture analysis, and a measurement method of an object surface roughness and the object surface displacement is subsequently put forward. In the geometrical information extraction technique, the speckle pattern images are taken by a very simple configuration of setup consisting of a laser and a CCD camera. Surface roughness and displacement information is extracted based on the energy feature characterization of the grey level co-occurrence matrices of the speckle pattern images. Our experimental results show that surface roughness and displacement contained in surface speckle pattern images have a good relationship with their energy feature. By means of a set of standard surface specimens with different roughness, the relation curve is readily calibrated. Then the surface roughness of an object surface made by same material and manufactured by the same way as the standard specimens can be evaluated from a single speckle pattern image taken from the surface. Similarly, the surface displacement can also be obtained from a single speckle pattern image. The easily implemented method can be used in-process surface roughness and displacement measurement.

Surface speckle pattern intensity distribution resulting from laser light scattering from a rough surface contains various information about the surface geometrical and physical properties. A surface roughness measurement technique based on the texture analysis of surface speckle pattern texture images is put forward. In the surface roughness measurement technique, the speckle pattern texture images are taken by a simple setup configuration consisting of a laser and a CCD camera. Our experimental results show that the surface roughness contained in the surface speckle pattern texture images has a good monotonic relationship with their energy feature of the gray-level co-occurrence matrices. After the measurement system is calibrated by a standard surface roughness specimen, the surface roughness of the object surface composed of the same material and machined by the same method as the standard specimen surface can be evaluated from a single speckle pattern texture image. The robustness of the characterization of speckle pattern texture for surface roughness is also discussed. Thus the surface roughness measurement technique can be used for an in-process surface measurement.

A hybrid vision system for online measurement of surface roughness is introduced. The hybrid vision system applies two cameras for capturing the laser speckle pattern and scattering images simultaneously. With the help of advanced image processing, several features of texture and shape are computed for the surface roughness characterization. On the basis of experimental tests, feature fusion to improve measurement range and linearization of the measurement is also discussed.