Browsing by Subject "Adaptive subband decomposition"

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Open Access
Adaptive polyphase subband decomposition structures for image compression
(IEEE, 2000) Gerek, Ö. N.; Çetin, A. Enis
Subband decomposition techniques have been extensively used for data coding and analysis. In most filter banks, the goal is to obtain subsampled signals corresponding to different spectral regions of the original data. However, this approach leads to various artifacts in images having spatially varying characteristics, such as images containing text, subtitles, or sharp edges. In this paper, adaptive filter banks with perfect reconstruction property are presented for such images. The filters of the decomposition structure which can be either linear or nonlinear vary according to the nature of the signal. This leads to improved image compression ratios. Simulation examples are presented.
Open Access
Image denoising using adaptive subband decomposition
(IEEE, 2001) Gezici, Sinan; Yılmaz, İsmail; Gerek, Ö. N.; Çetin, A. Enis
In this paper, we present a new image denoising method based on adaptive subband decomposition (or adaptive wavelet transform) in which the filter coefficients are updated according to an Least Mean Square (LMS) type algorithm. Adaptive subband decomposition filter banks have the perfect reconstruction property. Since the adaptive filterbank adjusts itself to the changing input environments denoising is more effective compared to fixed filterbanks. Simulation examples are presented.
Open Access
Moving object detection using adaptive subband decomposition and fractional lower-order statistics in video sequences
(Elsevier, 2002) Bagci, A. M.; Yardimci, Y.; Çetin, A. Enis
In this paper, a moving object 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 moving objects appear as outliers and they are detected using a statistical detection test based on fractional lower-order statistics. It turns out that the distribution of the subimage pixels is almost Gaussian in general. On the other hand, at the object boundaries the distribution of the pixels in the subimages deviates from Gaussianity due to the existence of outliers. By detecting the regions containing outliers the boundaries of the moving objects are estimated. Simulation examples are presented. © 2002 Elsevier Science B.V. All rights reserved.
Open Access
Small moving object detection using adaptive subband decomposition and fractional lower order statistics in video sequences
(2001-07-08) Bağci, A.Murat; Yardımcı, Y.; Çetin, A. Enis
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 highband subimages moving objects appear as outliers and they are detected using a statistical detection test based on lower 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. By detecting the regions containing outliers the boundaries of the moving objects are estimated. Simulation examples are presented.
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.