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Full-Text Articles in Engineering
Classification Of Infrasound Events Using Hermite Polynomial Preprocessing And Radial Basis Function Neural Networks, Christopher G. Lowrie
Classification Of Infrasound Events Using Hermite Polynomial Preprocessing And Radial Basis Function Neural Networks, Christopher G. Lowrie
Electrical Engineering and Computer Science Faculty Publications
A method of infrasonic signal classification using hermite polynomials for signal preprocessing is presented. Infrasound is a low frequency acoustic phenomenon typically in the frequency range 0.01 Hz to 10 Hz. Data collected from infrasound sensors are preprocessed using a hermite orthogonal basis inner product approach. The hermite preprocessed signals result in feature vectors that are used as input to a parallel bank of radial basis function neural networks (RBFNN) for classification. The spread and threshold values for each of the RBFNN are then optimized. Robustness of this classification method is tested by introducing unknown events outside the training set …
Imagery Chain Assessment For Feature Extraction, Rufus H. Cofer, Samuel Peter Kozaitis
Imagery Chain Assessment For Feature Extraction, Rufus H. Cofer, Samuel Peter Kozaitis
Electrical Engineering and Computer Science Faculty Publications
It is shown that the image chain has important effects upon the quality of feature extraction. Exact analytic ROC results are given for the case where arbitrary multivariate normal imagery is passed to a Bayesian feature detector designed for multivariate normal imagery with a diagonal covariance matrix. Plots are provided to allow direct visual inspection of many of the more readily apparent effects. Also shown is an analytic tradeoff that says doubling background contrast is equal to halving sensor to scene distance or sensor noise. It is also shown that the results provide a lower bound to the ROC of …
Linear Feature Detection Using Multiresolution Wavelet Filters, Samuel Peter Kozaitis, Somkait Udomhunsakul, Rufus H. Cofer, A. Agarawal, Shuwu Song
Linear Feature Detection Using Multiresolution Wavelet Filters, Samuel Peter Kozaitis, Somkait Udomhunsakul, Rufus H. Cofer, A. Agarawal, Shuwu Song
Electrical Engineering and Computer Science Faculty Publications
We detected roads in aerial imagery based on multiresolution linear feature detection. Our method used the products of wavelet coefficients at several scales to identify and locate linear features. After detecting possible road pixels, we used a shortest-path algorithm to identify roads. The multiresolution approach effectively increased the size of the region we examined when looking for possible road pixels and reduced the effect of noise. We found that our approach leads to an effective method for detecting roads in aerial imagery.
Multiple-Input Joint Transform Correlator For Wavelet Feature Extraction, Samuel Peter Kozaitis, Mark A. Getbehead
Multiple-Input Joint Transform Correlator For Wavelet Feature Extraction, Samuel Peter Kozaitis, Mark A. Getbehead
Electrical Engineering and Computer Science Faculty Publications
We describe a joint transform correlator (JTC) that uses multiple input images encoded in the spatial domain for multiwavelet feature extraction. We extend the theory of a JTC to multiple inputs, which enables various combinations of cross-correlations between input images to be performed. Furthermore, we provide experimental results for four inputs with an optically addressed spatial light modulator in the Fourier plane. In addition, it is possible that the space-bandwidth product for multiwavelet feature extraction can be made the same as for a two-input JTC. © 1998 Society of Photo-Optical Instrumentation Engineers.