Sampling of the Fourier transforms (FTs) of fingerprints is studied with neural networks to detect regions useful for their classification. Ring-wedge detector (RWD) is modified and simulated to sample such regions. The output of the detector is propagated through a three-layer backpropagation neural network (BPNN) for checking the classification performance. Modified detector's performance is also compared with that of RWD. It has been found that fingerprints scanned at 500 dpi resolution contain useful information for their classification in a band of width 20 pixels with inner radius approx. 60 pixels.
Calibration in fringe projection profilometry is investigated using neural networks (NNs) and several calibration planes whose positions in space are known. Radial basis function (RBF) based- and backpropagation neural networks (BPNNs) are compared for phase-to-depth conversion for phase planes calculated using Fourier transform profilometry (FTP) and phase locked loops (PLLs). Experimental results are presented.
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