Fu, Zhouyu; Robles-Kelly, Antonio; Tan, Robby; Caelli, Terry
In this paper, we propose a novel approach to object material identification in spectral imaging by combining the use of absorption features and statistical machine learning techniques. We depart from the significance of spectral absorption features for material identification and cast the problem into a classification setting which can be tackled using support vector machines. Hence, we commence by proposing a novel method for the robust detection of absorption bands in the spectra. With these...[Show more]
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