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

• Published in: Machine vision and applications 2023-09, Vol.34 (5), p.90, Article 90
• Main Authors: Lang, Shinan, Chen, Fangyi, Cai, Yiheng
• Format: Article
• Language: English
• Subjects: Cameras; Classification; Communications Engineering; Computer Science; Computer vision; Image Processing and Computer Vision; Infrared imagery; Luminous intensity; Networks; Original Paper; Pattern Recognition; Reflectance; Refractivity; Service robots; Transmissivity; Vision systems
• ISSN: 0932-8092; 1432-1769
• DOI: 10.1007/s00138-023-01443-w

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.