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Highly transparent material classification using the refractive index, reflectivity, and transmissivity features from an imaging model of a time-of-flight camera

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Abstract

Highly transparent material classification can play an important role in the field of computer vision to classify glass or plastics for recycling and for home service robots to recognize transparent material. In these areas, there is a need to classify materials that are more than 73% transparent, but current transparent material classification methods cannot classify materials with full transparency levels. This paper proposes a highly transparent material classification method based on the refractive index, reflectivity, and transmissivity features from an imaging model of a time-of-flight (ToF) camera as the classification feature. First, we use the ToF camera to collect the depth and light intensity of the transparent material, as well as the scene information. The acquisition depth is distorted owing to the material characteristics of transparent materials. Second, we estimate the refractive index, reflectance, and transmittance from the depth distortion and IR (infrared rays) image. Finally, we choose a classifier that conforms to the nonlinear characteristics of the data to achieve transparent material classification. The method’s classification accuracy reached 94.1% in an experiment, indicating that our method considers the unique phenomenon of highly transparent materials reflecting against the background, incorporates this phenomenon into the ToF distance model, it can extract material features that express the characteristics of highly transparent materials, making it applicable to the classification of transparent materials at all levels of transparency.

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Acknowledgements

This work was supported by the Scientific Research Project of Beijing Educational Committee (No. KM 201910005027). The authors thank the anonymous referee for invaluable comments and suggestions.

Funding

This work was supported by the Scientific Research Project of Beijing Educational Committee (No. KM 201910005027).

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  1. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, People’s Republic of China

    Shinan Lang, Fangyi Chen & Yiheng Cai

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  1. Shinan Lang
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  2. Fangyi Chen
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  3. Yiheng Cai
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Correspondence to Yiheng Cai.

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Lang, S., Chen, F. & Cai, Y. Highly transparent material classification using the refractive index, reflectivity, and transmissivity features from an imaging model of a time-of-flight camera. Machine Vision and Applications 34, 90 (2023). https://doi.org/10.1007/s00138-023-01443-w

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  • DOI: https://doi.org/10.1007/s00138-023-01443-w

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