Browsing by Subject "Higher order statistics"

Now showing 1 - 4 of 4

Open Access
Characterization of sleep spindles using higher order statistics and spectra
(IEEE, 2000-08) Akgül, T.; Sun, M.; Sclabassi, R. J.; Çetin, A. Enis
This work characterizes the dynamics of sleep spindles, observed in electroencephalogram (EEG) recorded from humans during sleep, using both time and frequency domain methods which depend on higher order statistics and spectra. The time domain method combines the use of second- and third-order correlations to reveal information on the stationarity of periodic spindle rhythms to detect transitions between multiple activities. The frequency domain method, based on normalized spectrum and bispectrum, describes frequency interactions associated with nonlinearities occuring in the observed EEG.
Open Access
Detection of microcalcifications in mammograms using higher order statistics
(Institute of Electrical and Electronics Engineers, 1997-08) Gürcan, M. N.; Yardımcı, Y.; Çetin, A. Enis; Ansari, R.
A new method for detecting microcalcifications in mammograms is described. In this method, the mammogram image is first processed by a subband decomposition filterbank. The bandpass subimage is divided into overlapping square regions in which skewness and kurtosis as measures of the asymmetry and impulsiveness of the distribution are estimated. The detection method utilizes these two parameters. A region with high positive skewness and kurtosis is marked as a region of interest. Simulation results show that this method is successful in detecting regions with microcalcifications.
Open Access
Detection of microcalcifications in mammograms using local maxima and adaptive wavelet transform analysis
(The Institution of Engineering and Technology, 2002-10-24) Bagci, A. M.; Çetin, A. Enis
A method for computer-aided diagnosis of microcalcification clusters in mammogram images is presented. Microcalcification clusters which are an early sign of breast cancer appear as isolated bright spots in mammograms. Therefore they correspond to local maxima of the image. The local maxima of the image is first detected and they are ranked according to a higher-order statistical test performed over the subband domain data.
Open Access
Small moving object detection using adaptive subband decomposition in video sequences
(SPIE, 2000) Zaibi, Rabi; Çetin, A. Enis; Yardımcı, Y. C.
In this paper, a small moving object method detection method in video sequences is described. In the first step, the camera motion is eliminated using motion compensation. An adaptive subband decomposition structure is then used to analyze the motion compensated image. In the 'low-high' and 'high-low' subimages small moving objects appear as outliers and they are detected using a statistical Gaussianity detection test based on higher order statistics. It turns out that in general, the distribution of the residual error image pixels is almost Gaussian. On the other hand, the distribution of the pixels in the residual image deviates from Gaussianity in the existence of outliers. Simulation examples are presented.