The Experts below are selected from a list of 324 Experts worldwide ranked by ideXlab platform
Joseph Ronsin - One of the best experts on this subject based on the ideXlab platform.
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planar shapes descriptors based on the turning angle Scalogram
International Conference on Image Analysis and Recognition, 2008Co-Authors: Kidiyo Kpalma, Minqiang Yang, Joseph RonsinAbstract:In a previous paper, we have presented an original approach for 2-D shapes representation. Based on a multi-scale analysis of closed contours, this method deals with the Scalogram of the differential turning angle. We then showed that this representation is rotation, translation and scale change invariant and that it is also shearing and noise resistant. In this paper, we propose some features extracted from this Scalogram. This enables us to evaluate the turning angle Scalogram representation of planar objects in the context of pattern recognition. When applied to shape retrieval from a database and for various transformations (deformations), experimental results confirm the efficiency of this new description approach.
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ICIAR - Planar Shapes Descriptors Based on the Turning Angle Scalogram
Lecture Notes in Computer Science, 1Co-Authors: Kidiyo Kpalma, Minqiang Yang, Joseph RonsinAbstract:In a previous paper, we have presented an original approach for 2-D shapes representation. Based on a multi-scale analysis of closed contours, this method deals with the Scalogram of the differential turning angle. We then showed that this representation is rotation, translation and scale change invariant and that it is also shearing and noise resistant. In this paper, we propose some features extracted from this Scalogram. This enables us to evaluate the turning angle Scalogram representation of planar objects in the context of pattern recognition. When applied to shape retrieval from a database and for various transformations (deformations), experimental results confirm the efficiency of this new description approach.
Kidiyo Kpalma - One of the best experts on this subject based on the ideXlab platform.
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planar shapes descriptors based on the turning angle Scalogram
International Conference on Image Analysis and Recognition, 2008Co-Authors: Kidiyo Kpalma, Minqiang Yang, Joseph RonsinAbstract:In a previous paper, we have presented an original approach for 2-D shapes representation. Based on a multi-scale analysis of closed contours, this method deals with the Scalogram of the differential turning angle. We then showed that this representation is rotation, translation and scale change invariant and that it is also shearing and noise resistant. In this paper, we propose some features extracted from this Scalogram. This enables us to evaluate the turning angle Scalogram representation of planar objects in the context of pattern recognition. When applied to shape retrieval from a database and for various transformations (deformations), experimental results confirm the efficiency of this new description approach.
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ICIAR - Planar Shapes Descriptors Based on the Turning Angle Scalogram
Lecture Notes in Computer Science, 1Co-Authors: Kidiyo Kpalma, Minqiang Yang, Joseph RonsinAbstract:In a previous paper, we have presented an original approach for 2-D shapes representation. Based on a multi-scale analysis of closed contours, this method deals with the Scalogram of the differential turning angle. We then showed that this representation is rotation, translation and scale change invariant and that it is also shearing and noise resistant. In this paper, we propose some features extracted from this Scalogram. This enables us to evaluate the turning angle Scalogram representation of planar objects in the context of pattern recognition. When applied to shape retrieval from a database and for various transformations (deformations), experimental results confirm the efficiency of this new description approach.
Ilan Sharon - One of the best experts on this subject based on the ideXlab platform.
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partial order Scalogram analysis of relations a mathematical approach to the analysis of stratigraphy
Journal of Archaeological Science, 1995Co-Authors: Ilan SharonAbstract:Abstract The use of partial Scalogram analysis is suggested as an alternative to Harris matrix analysis for the phasing of complexly stratified sites. This paper contains a formal mathematical presentation of the partial order Scalogram analysis of relations (POSAR) model. Two algorithms for obtaining optimal solutions to the POSAR problem-steepest descent and stimulated annealing—are compared, and several computational and Archaeological problems pertaining to the mathematical modelling of stratigraphic analysis are discussed.
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Partial order Scalogram analysis of relations—a mathematical approach to the analysis of stratigraphy
Journal of Archaeological Science, 1995Co-Authors: Ilan SharonAbstract:Abstract The use of partial Scalogram analysis is suggested as an alternative to Harris matrix analysis for the phasing of complexly stratified sites. This paper contains a formal mathematical presentation of the partial order Scalogram analysis of relations (POSAR) model. Two algorithms for obtaining optimal solutions to the POSAR problem-steepest descent and stimulated annealing—are compared, and several computational and Archaeological problems pertaining to the mathematical modelling of stratigraphic analysis are discussed.
François Roueff - One of the best experts on this subject based on the ideXlab platform.
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Large scale reduction principle and application to hypothesis testing
Electronic journal of statistics, 2015Co-Authors: Marianne Clausel, François Roueff, Murad S TaqquAbstract:Consider a non-linear function $G(X_t)$ where $X_t$ is a stationary Gaussian sequence with long-range dependence. The usual reduction principle states that the partial sums of $G(X_t)$ behave asymptotically like the partial sums of the first term in the expansion of $G$ in Hermite polynomials. In the context of the wavelet estimation of the long-range dependence parameter, one replaces the partial sums of $G(X_t)$ by the wavelet Scalogram, namely the partial sum of squares of the wavelet coefficients. Is there a reduction principle in the wavelet setting, namely is the asymptotic behavior of the Scalogram for $G(X_t)$ the same as that for the first term in the expansion of $G$ in Hermite polynomial? The answer is negative in general. This paper provides a minimal growth condition on the scales of the wavelet coefficients which ensures that the reduction principle also holds for the Scalogram. The results are applied to testing the hypothesis that the long-range dependence parameter takes a specific value.
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Asymptotic Statistics for the Scalogram of a Time Series
2010Co-Authors: François RoueffAbstract:The Scalogram of a time series is defined as the quadratic mean of a multiscale analysis. The latter can be obtained using a wavelet analysis, or, more generally, using a sequence of filters of increasing scales, each one being followed by a temporal subsampling of order proportional to the analyzing scale. We will describe the asymptotic behavior of the Scalogram as the scale and the number of observations tend to infinity, for an increment-stationary linear process with short or long memory, and of some non-linear or/and non-stationary long-memory processes.
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asymptotic normality of wavelet estimators of the memory parameter for linear processes
Journal of Time Series Analysis, 2009Co-Authors: François Roueff, Murad S TaqquAbstract:We consider linear processes, not necessarily Gaussian, with long, short or negative memory. The memory parameter is estimated semi-parametrically using wavelets from a sample X_1, H , X_n of the process. We treat both the log-regression wavelet estimator and the wavelet Whittle estimator. We show that these estimators are asymptotically normal as the sample size n goes to infinity and we obtain an explicit expression for the limit variance. These results are derived from a general result on the asymptotic normality of the Scalogram for linear processes, conveniently centred and normalized. The Scalogram is an array of quadratic forms of the observed sample, computed from the wavelet coefficients of this sample. In contrast to quadratic forms computed on the basis of Fourier coefficients such as the periodogram, the Scalogram involves correlations which do not vanish as the sample size n goes to infinity. Copyright 2009 Blackwell Publishing Ltd
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asymptotic normality of wavelet estimators of the memory parameter for linear processes
arXiv: Statistics Theory, 2008Co-Authors: François Roueff, Murad S TaqquAbstract:We consider linear processes, not necessarily Gaussian, with long, short or negative memory. The memory parameter is estimated semi-parametrically using wavelets from a sample $X_1,...,X_n$ of the process. We treat both the log-regression wavelet estimator and the wavelet Whittle estimator. We show that these estimators are asymptotically normal as the sample size $n\to\infty$ and we obtain an explicit expression for the limit variance. These results are derived from a general result on the asymptotic normality of the empirical Scalogram for linear processes, conveniently centered and normalized. The Scalogram is an array of quadratic forms of the observed sample, computed from the wavelet coefficients of this sample. In contrast with quadratic forms computed on the Fourier coefficients such as the periodogram, the Scalogram involves correlations which do not vanish as the sample size $n\to\infty$.
Murad S Taqqu - One of the best experts on this subject based on the ideXlab platform.
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Large scale reduction principle and application to hypothesis testing
Electronic journal of statistics, 2015Co-Authors: Marianne Clausel, François Roueff, Murad S TaqquAbstract:Consider a non-linear function $G(X_t)$ where $X_t$ is a stationary Gaussian sequence with long-range dependence. The usual reduction principle states that the partial sums of $G(X_t)$ behave asymptotically like the partial sums of the first term in the expansion of $G$ in Hermite polynomials. In the context of the wavelet estimation of the long-range dependence parameter, one replaces the partial sums of $G(X_t)$ by the wavelet Scalogram, namely the partial sum of squares of the wavelet coefficients. Is there a reduction principle in the wavelet setting, namely is the asymptotic behavior of the Scalogram for $G(X_t)$ the same as that for the first term in the expansion of $G$ in Hermite polynomial? The answer is negative in general. This paper provides a minimal growth condition on the scales of the wavelet coefficients which ensures that the reduction principle also holds for the Scalogram. The results are applied to testing the hypothesis that the long-range dependence parameter takes a specific value.
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asymptotic normality of wavelet estimators of the memory parameter for linear processes
Journal of Time Series Analysis, 2009Co-Authors: François Roueff, Murad S TaqquAbstract:We consider linear processes, not necessarily Gaussian, with long, short or negative memory. The memory parameter is estimated semi-parametrically using wavelets from a sample X_1, H , X_n of the process. We treat both the log-regression wavelet estimator and the wavelet Whittle estimator. We show that these estimators are asymptotically normal as the sample size n goes to infinity and we obtain an explicit expression for the limit variance. These results are derived from a general result on the asymptotic normality of the Scalogram for linear processes, conveniently centred and normalized. The Scalogram is an array of quadratic forms of the observed sample, computed from the wavelet coefficients of this sample. In contrast to quadratic forms computed on the basis of Fourier coefficients such as the periodogram, the Scalogram involves correlations which do not vanish as the sample size n goes to infinity. Copyright 2009 Blackwell Publishing Ltd
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asymptotic normality of wavelet estimators of the memory parameter for linear processes
arXiv: Statistics Theory, 2008Co-Authors: François Roueff, Murad S TaqquAbstract:We consider linear processes, not necessarily Gaussian, with long, short or negative memory. The memory parameter is estimated semi-parametrically using wavelets from a sample $X_1,...,X_n$ of the process. We treat both the log-regression wavelet estimator and the wavelet Whittle estimator. We show that these estimators are asymptotically normal as the sample size $n\to\infty$ and we obtain an explicit expression for the limit variance. These results are derived from a general result on the asymptotic normality of the empirical Scalogram for linear processes, conveniently centered and normalized. The Scalogram is an array of quadratic forms of the observed sample, computed from the wavelet coefficients of this sample. In contrast with quadratic forms computed on the Fourier coefficients such as the periodogram, the Scalogram involves correlations which do not vanish as the sample size $n\to\infty$.
