Discriminative Illumination: Per-Pixel Classification of Raw Materials Based on Optimal Projections of Spectral BRDF
Issue No. 01 - Jan. (2014 vol. 36)
Chao Liu , Center for Imaging Sci., Rochester Inst. of Technol., Rochester, NY, USA
Jinwei Gu , Center for Imaging Sci., Rochester Inst. of Technol., Rochester, NY, USA
Classifying raw, unpainted materials--metal, plastic, ceramic, fabric, and so on--is an important yet challenging task for computer vision. Previous works measure subsets of surface spectral reflectance as features for classification. However, acquiring the full spectral reflectance is time consuming and error-prone. In this paper, we propose to use coded illumination to directly measure discriminative features for material classification. Optimal illumination patterns--which we call "discriminative illumination"--are learned from training samples, after projecting to which the spectral reflectance of different materials are maximally separated. This projection is automatically realized by the integration of incident light for surface reflection. While a single discriminative illumination is capable of linear, two-class classification, we show that multiple discriminative illuminations can be used for nonlinear and multiclass classification. We also show theoretically that the proposed method has higher signal-to-noise ratio than previous methods due to light multiplexing. Finally, we construct an LED-based multispectral dome and use the discriminative illumination method for classifying a variety of raw materials, including metal (aluminum, alloy, steel, stainless steel, brass, and copper), plastic, ceramic, fabric, and wood. Experimental results demonstrate its effectiveness.
Lighting, Light emitting diodes, Signal to noise ratio, Raw materials, Metals,material classification, Computational illumination, appearance modeling
Chao Liu, Jinwei Gu, "Discriminative Illumination: Per-Pixel Classification of Raw Materials Based on Optimal Projections of Spectral BRDF", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 36, no. , pp. 86-98, Jan. 2014, doi:10.1109/TPAMI.2013.110