Resumen
In the industrial manufacturing of transparent semi-transparent materials, such as glass, plastic, resin, crystal or liquid, have wide application use. The cleanliness and purity of these components have very strict requirements, such as smart phone cover plates, liquid crystal display panels and camera lenses. The environments for manufacture process of above-mentioned industrial fields usually have high-standard requirement of cleanliness. Impurities or contaminants, such as fiber and dust, are defects that have to be identified, analyzed and controlled during the process. Although defect detection based on machine vision and neural network has made great progress, performing tracking, measurement or identification of targets of interests in a volume is still very challenging. This demand has already become much more urgent and favored by industrial customers to control the manufacturing quality of the multi-layer structure of glass, plastic or other composite material. Meanwhile, in biomedical industries, transparent or translucid materials or medium are in common use. Various targets of interest in a solution or other medium needs to be tracked, identified and analyzed. In this sense, the proposed Autostereoscopy-Raman Spectrometry-based (ARS) measurement methodology and its developed system in this paper are able to be widely applied to but not limited to the production quality control of multilayer glass structure, the manufacture and assembly of precision optical components, the production process control of liquid crystal panel in a clean room or in a laboratory testing, or analyzing equipment for biomedical applications.