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The 3D multispectral imaging system proposed in this contribution realizes simultaneous detection of contaminants and 3D localization. The imaging system is composed of a digital pattern projector, a stereo-vision setup of two multispectral filter wheel cameras, and an external light source. A calibration procedure is developed to estimate simultaneously stereo camera parameters and light source parameters. For an acceleration of image acquisition, the entire spectral range is split into two parts which are captured from two different camera views and merged using structured light. The usefulness of the proposed system is demonstrated with an example of cutting oil detection. For this the fluorescence effect is utilized, and specular reflections are filtered out based on the estimation of illumination geometry. Experimental results show that the surface areas with cutting oil could be reliably distinguished from workpieces using the proposed algorithms.
Chen Zhang,Maik Rosenberger, andGunther Notni
"3D multispectral imaging system for contamination detection", Proc. SPIE 11056, Optical Measurement Systems for Industrial Inspection XI, 1105618 (21 June 2019); https://doi.org/10.1117/12.2525903
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Chen Zhang, Maik Rosenberger, Gunther Notni, "3D multispectral imaging system for contamination detection," Proc. SPIE 11056, Optical Measurement Systems for Industrial Inspection XI, 1105618 (21 June 2019); https://doi.org/10.1117/12.2525903