Proceedings PaperOptical classification of metal-milled samples using Fourier spectrum sampling
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An optical/digital approach to the classification of rough surfaces using Fourier spectrum sampling is described. The sampling of the 2-D Fourier spectrum is achieved with wedge ring detector which reduces an infinitely dimensioned spectrum image into a set of 64 measurements. To discriminate three metal milled samples in this reduced subspace we employ the Karhunen-Loève transformation. The classification procedure then selects automatically the best subspace from the K-L feature vectors.