4 February 2013 Metal-dielectric object classification by combining polarization property and surface spectral reflectance
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Abstract
We propose a method for automatically classifying multiple objects in a natural scene into metal or dielectric. We utilize polarization property in order to classify the objects into metal and dielectric, and surface-spectral reflectance in order to segment the scene image into different object surface regions. An imaging system is developed using a liquid crystal tunable filter for capturing both polarization and spectral images simultaneously. Our classification algorithm consists of three stages; (1) highlight detection based on luminance threshold, (2) material classification based on the spatial distribution of the degree of polarization at the highlight area, and (3) image segmentation based on illuminant-invariant representation of the spectral reflectance. The feasibility of the proposed method is examined in detail in experiments using real-world objects.
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Shoji Tominaga, Hideki Kadoi, Keita Hirai, Takahiko Horiuchi, "Metal-dielectric object classification by combining polarization property and surface spectral reflectance", Proc. SPIE 8652, Color Imaging XVIII: Displaying, Processing, Hardcopy, and Applications, 86520E (4 February 2013); doi: 10.1117/12.2005638; https://doi.org/10.1117/12.2005638
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