RESUMEN
The authors designed a self-adaptive projection system which is composed of color camera, projector and PC. In detail, digital micro-mirror device (DMD) as a spatial light modulator for the projector was introduced in the optical path to modulate the illuminant spectrum based on red, green and blue light emitting diodes (LED). However, the color visibility of active markers is affected by the screen which has unknown reflective spectrum as well. Here active markers are projected spot array. And chromaticity feature of markers is sometimes submerged in similar spectral screen. In order to enhance the color visibility of active markers relative to screen, a method for selecting self-adaptive chromaticity of the projected markers in 3D scanning metrology is described. Color camera with 3 channels limits the accuracy of device characterization. For achieving interconversion of device-independent color space and device-dependent color space, high-dimensional linear model of reflective spectrum was built. Prior training samples provide additional constraints to yield high-dimensional linear model with more than three degrees of freedom. Meanwhile, spectral power distribution of ambient light was estimated. Subsequently, markers' chromaticity in CIE color spaces was selected via maximization principle of Euclidean distance. The setting values of RGB were easily estimated via inverse transform. Finally, we implemented a typical experiment to show the performance of the proposed approach. An 24 Munsell Color Checker was used as projective screen. Color difference in the chromaticity coordinates between the active marker and the color patch was utilized to evaluate the color visibility of active markers relative to the screen. The result comparison between self-adaptive projection system and traditional diode-laser light projector was listed and discussed to highlight advantage of our proposed method.
RESUMEN
For no cooperation target laser ranging, the backscattering properties of the long-range and real machined surfaces are uncertain which seriously affect the ranging accuracy. It is an important bottleneck restricting the development of no cooperation ranging technology. In this paper, the backscattering characteristics of three typical machining surfaces (vertidal milling processing method, horizontal milling processing method and plain grinding processing method) under the infrared laser irradiation with 1550 nm were measured. The relation between the surface nachining texture, incident azimuth, roughness and the backscattering distribution were analyzed and the reasons for different processing methods specific backscattering field formed were explored. The experimental results show that the distribution of backscattering spectra is greatly affected by the machined processing methods. Incident angle and roughness have regularity effect on the actual rough surface of each mode. To be able to get enough backscattering, knowing the surface texture direction and the roughness of machined metal is essential for the optimization of the non-contact measurement program in industry. On this basis, a method based on an artificial neural network (ANN) and genetic algorithm (GA), is proposed to retrieve the surface multi-parameters of the machined metal. The generalized regression neural network (GRNN) was investigated and used in this application for the backscattering modeling. A genetic algorithm was used to retrieve the multi-parameters of incident azimuth angle, roughness and processing methods of machined metal sur face. Another processing method of sample (planer processing method) was used to validate data. The final results demonstrated that the method presented was efficient in parameters retrieval tasks. This model can accurately distinguish processing methods and the relative error of incident azimuth and roughness is 1.21% and 1.03%, respectively. The inversion accuracy is high. It can reduce the impact of surface texture, the incident azimuth and incidence angle to the ranging scope. The experiments proved that the inversion of the surface parameters greatly broadened the ranging scope in no cooperation target laser ranging. Taking the Vertical milling sample with roughness Ra=6.3 microm for example, the measuring range can be increased by about 22 m when the incidence angle is increased in the incidence plane which is vertical to the surface texture. The study results of this paper have a certain reference value to the research of the backscattering of machined surface and its application in other areas.
RESUMEN
In color vision application, for the images with multiple colors, object can not be clearly separated from complicated background. The discrimination of the surfaces can be enhanced by selecting appropriate wavelength intervals of illumination. Firstly, the reflectance functions of all the surfaces were calibrated by four standard references. Then partial least squares method was used for selecting the effective wavelength interval of illumination. The variables important in projection (VIP) scores of wavelength intervals were considered as selection criteria. Wavelength intervals with the VIP > 1.0 were selected for illumination. Finally, three effective wavelength intervals of LED illumination were selected to separate all the surfaces of experiment image simultaneously. For separating all the surfaces of experiment image simultaneously and improving discrimination, the experiment was carried out. The experiment results demonstrate the usefulness of this method.
RESUMEN
A common-path laser interferometer is introduced. Its most important feature is that two optical arms are entirely in a common path. Laser Doppler and laser polarization interference technologies are used in the proposed optical system. An experiment setup is established. The primary experimental result shows that this common path method is very feasible. The resolution is 0.72 nm within a 0.01 mm range, and the highest velocity is 0.56 mm/s. There is much room to improve its performance.