Understanding multi-spectral images of wood particles with matrix factorization
By M. Asbach, D. Mauruschat, B. Plinke. 2013
Multispectral image data can be used to quantify the concentrations of chemical substances in material compounds by differential spectroscopy. In this paper, we describe Simplex Volume Maximization (SiVM), a matrix factorization method derived from Archetypal Analysis (AA), that is well suited to separate spectra. Exemplarily, we apply the technique to multispectral images of wood strands partially covered with adhesives and wood-polymer composites and show how to determine the concentration of the adhesives and how to distinguish the polymer types. In the multispectral domain, our objective is to separate the spectral characteristics of the adhesives and polymers from those spectral components caused by variation in the natural wood, including differences in moisture. Our experiments show that wood particles with different concentrations of adhesives or different polymer components can be distinguished after applying SiVM-based factorization to NIR spectral imaging. We therefore conclude that this technique has great potential for quality control applications that rely on multispectral imaging.
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Citations in the corpus
- Prototypal Analysis and Prototypal Regression (2017, background): “…Bauckhage, 2013), marketing (Li et al., 2003; DEsposito et al., 2006), performance analysis (Porzio et al., 2006, 2008; Eugster, 2012; Seiler and Wohlrabe, 2013) and computer vision (Marinetti et al., 2006; Thurau and Bauckhage, 2009; Cheema et al., 2011; Asbach et al., 2013; Xiong et al., 2013).”