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.

On-line roughness measurement of a surface with one-dimensional manufacturing marks is difficult to implement. For example, a contact stylus-type inspection method often does not perform very well or fails without any prior knowledge of the mark distribution on the surface. In this paper, we propose an on-line surface roughness measurement method based on laser light scattering, which is very effective for roughness measurement of one-dimensional manufacturing surfaces. The surface roughness is obtained from the spatial distribution of the scattered light intensity. The measurement setup has a very simple configuration, which consists of a CCD sensor, a collimated diode laser and an expander. The orientation of the spatial distribution of the scattered light intensity from the surface, which depends on the surface orientation, is detected by the CCD sensor, and then the mark direction can be readily determined from image processing. After that the root-mean-square (RMS) height of the surface roughness is extracted by means of image processing of the scattered light distribution in the direction parallel to the manufacturing mark, rather than in the direction perpendicular to the mark which is often followed by other measurement probes. The experimental tests show that the non-contact method has great potential for on-line surface roughness measurement.

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.

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.

A surface scanner has been developed which records the light from an approximately flat surface at up to sixteen different angles. The scanner simultaneously records images of the surface as an illuminated spot is traversed across the surface. The optical design of the scanner allows the use of just one parabolic mirror slightly larger than the scan length required, and small detectors for the reflected/scattered light at each angle. Design principles for the scanner are developed and some resulting images on surfaces presented. The data from the above scanner is used to compute the surface topology of a surface by integrating the surface slope measurements obtained from the multi-angle data. This is accomplished by obtaining an estimation of surface slope at each pixel position. Constraints are then imposed on the data to obtain a continuous three-dimensional surface. The surface obtained is compared with data obtained from the surface by measuring with a “Talysurf” stylus measurement machine. To do this a common coordinate frame is needed requiring a rotation and scaling in two directions of one of the data sets to obtain maximum cross-correlation between the three dimensional profiles.