INVESTIGATING THE LIMITS OF THE AUTOREGRESSIVE MODEL for 1D and 2D SIGNAL PROCESSING TECHNIQUES

Abstract : Even if the Autoregressive (AR) model is widely used in several frameworks such as speech, seismic and biomedical or textured image analysis, it is not really efficient to represent periodical signals. In this paper we propose first to investigate the limits of the autoregressive process and secondly to present an alternative approach based on the Wold decomposition. We then present a comparative study between AR model and Wold decomposition in the framework of the reconstruction of textured images
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https://hal.archives-ouvertes.fr/hal-00167774
Contributor : Eric Grivel <>
Submitted on : Wednesday, August 22, 2007 - 4:50:49 PM
Last modification on : Thursday, January 11, 2018 - 6:21:07 AM

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  • HAL Id : hal-00167774, version 1

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Clarysse Ramananjarasoa, Eric Grivel, Mohamed Najim. INVESTIGATING THE LIMITS OF THE AUTOREGRESSIVE MODEL for 1D and 2D SIGNAL PROCESSING TECHNIQUES. JTEA, 2002, Sousse, Tunisia. pp. ⟨hal-00167774⟩

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