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Showing papers by "Ioannis Pitas published in 1991"


Journal ArticleDOI
TL;DR: A family of multichannel filters based on multivariate data ordering, such as the marginal Median, the vector median, the marginal alpha -trimmed mean, and the multich channel modified trimmed mean filter, is described in detail.
Abstract: Multivariate data ordering and its use in color image filtering are presented. Several of the filters presented are extensions of the single-channel filters based on order statistics. The statistical analysis of the marginal order statistics is presented for the p-dimensional case. A family of multichannel filters based on multivariate data ordering, such as the marginal median, the vector median, the marginal alpha -trimmed mean, and the multichannel modified trimmed mean filter, is described in detail. The performance of the marginal median and the vector median filters in impulsive noise filtering is investigated. Simulation examples of the filters under study are described. >

178 citations


Journal ArticleDOI
TL;DR: An adaptive filter structure which is based on linear combinations of order statistics which can adapt well to a variety of noise probability distributions, including impulsive noise and is suitable for image-processing applications.
Abstract: An adaptive filter structure which is based on linear combinations of order statistics is proposed. An efficient method to update the filter coefficients is presented, which is based on the minimal mean-square error criterion and which is similar to the Widrow algorithm for the linear adaptive filters. Another method for coefficient update is presented, which is similar to the recursive least squares (RLS) algorithm and which has faster convergence properties. The proposed-filter can adapt well to a variety of noise probability distributions, including impulsive noise. It also performs well in the case of nonstationary signals and, therefore, it is suitable for image-processing applications. >

54 citations


Journal ArticleDOI
TL;DR: The objects contained in the range images are decomposed into simpler parts by using the morphological decomposition algorithm of grayscale images to use this decomposition in a recognition algorithm suitable for range images.

33 citations


Proceedings ArticleDOI
14 Apr 1991
TL;DR: A new approach for shape representation is described which provides a general scheme for object description and unifies some of the existing representation techniques, based on the use of simple geometric objects which are intuitively used by humans in their perception of shapes and on mathematical morphology.
Abstract: A new approach for shape representation is described which provides a general scheme for object description and unifies some of the existing representation techniques (e.g. CSG, skeletons, shape decomposition). It is based on the use of simple geometric objects which are intuitively used by humans in their perception of shapes and on mathematical morphology. Furthermore, its unifying framework can be used both in computer graphics and computer vision, thus providing a tool to close the existing gap between object modeling and object recognition in certain applications, e.g. in robotic vision. >

20 citations


Proceedings ArticleDOI
14 Apr 1991
TL;DR: Two novel adaptive nonlinear filter structures are proposed which are based on linear combinations of order statistics and have the ability to incorporate constraints imposed on coefficients in order to permit location invariant and unbiased estimation of a constant signal in the presence of additive white noise.
Abstract: Two novel adaptive nonlinear filter structures are proposed which are based on linear combinations of order statistics. These adaptive schemes are modifications of the standard LMS (least mean square) algorithm and have the ability to incorporate constraints imposed on coefficients in order to permit location invariant and unbiased estimation of a constant signal in the presence of additive white noise. The convergence properties of the proposed filters are considered. Both of them can adapt well to a variety of noise probability distributions ranging from short-tailed to long-tailed ones. Simulation examples are given. >

14 citations


Journal ArticleDOI
TL;DR: A novel class of nonlinear adaptive filters based on order statistics is presented and an LMS algorithm for their adaptation is proposed, essentially a backpropagation algorithm for the adaptation of coefficients that are used before data sorting.

11 citations


Book ChapterDOI
01 Jan 1991
TL;DR: Two novel signal-adaptive nonlinear filters are proposed for speckle reduction in ultrasound B-mode images that are based on the L 2 mean filter which is the maximum likelihood (ML) estimator of a constant signal corrupted by multiplicative Rayleigh noise.
Abstract: Two novel signal-adaptive nonlinear filters are proposed for speckle reduction in ultrasound B-mode images. The first is a modification of the Signal-Adaptive Median, which uses the Fractal Dimension (FD) as a measure of the local signal activity. The second one is based on the L 2 mean filter which is the maximum likelihood (ML) estimator of a constant signal corrupted by multiplicative Rayleigh noise and uses the above-mentioned measure of local signal activity. The proposed signal adaptive nonlinear filters suppress the speckle noise while preserving the image texture.

3 citations


Proceedings ArticleDOI
22 May 1991
TL;DR: Two adaptive nonlinear filter structures are proposed which are based on linear combinations of order statistics and have the ability to incorporate constraints imposed on coefficients in order to permit location-invariant and unbiased estimation of a constant signal in the presence of additive white noise.
Abstract: Two adaptive nonlinear filter structures are proposed which are based on linear combinations of order statistics. These adaptive schemes are modifications of the standard LMS algorithm and have the ability to incorporate constraints imposed on coefficients in order to permit location-invariant and unbiased estimation of a constant signal in the presence of additive white noise. The convergence in the mean of the filter coefficients is proven. The proposed filters can adapt well to a variety of noise probability distributions ranging from short-tailed to long-tailed distributions. >

1 citations