Surface classification by an optoelectronic implementation of the Karhunen-Loève expansion.
An optical-digital approach to the classification of rough surfaces that uses a Fourier-transform feature space is described. The sampling of the two-dimensional Fourier spectrum is achieved with a charge-coupled device detector array, which has a polar-sampling geometry and reduces an infinitely dimensioned spectrum image into a set of 72 measurements. To discriminate among three plastic samples in this reduced subspace, we use the Karhunen-Loève transformation. Then the classification procedure automatically selects the best subspace from the Karhunen-Loève vectors.
Gorecki
C
C