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Transparent material polishing surface classification based on the microfacet shape factor, reflectivity, and transmissivity features using an imaging model of a time-of-flight camera

  • Shinan Lang,
  • Fangyi Chen,
  • Yiheng Cai,
  • Qiang Wu

摘要

Classification of transparent materials with various roughness types has been widely used in the field of computer vision. However, the surface roughness of a transparent material affects the extraction effect of classification features, thus affecting the performance of transparent material classification. In this study, a classification method of transparent materials with various surface roughness types and transparencies, which uses the microfacet shape factor, reflectivity, and transmissivity as classification characteristics, is proposed. First, a transparent material feature extraction method based on microfacet distribution function is proposed for the first time, and the microfacet shape factor, reflectivity, and transmissivity are extracted by our model as classification features. The microfacet distribution model ground glass unknown is combined with the time-of-flight imaging model to achieve an accurate classification of surfaces with various roughness types. Then, according to the nonlinear and discrete characteristics of data, an appropriate classifier is selected to realize the transparent material classification. The transparent material classification experiments are performed using four types of material appearances, and the proposed method is compared with the methods of Shim et al. and Lang et al. The average classification accuracy of the proposed method for the transparent materials with four material appearances is 92.62%, which represents improvements of 56.18% and 11.58% compared with the methods of Shim et al. and Lang et al., respectively. These improvements are achieved because the proposed method uses the microfacet shape factors as classification features, which ensures that the classification effect of transparent materials is not affected by surface roughness. Finally, the proposed method is suitable for all transparent materials.