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Subspace System Identification of Separable-in-Denominator 2-D Stochastic Systems

Title
Subspace System Identification of Separable-in-Denominator 2-D Stochastic Systems
Type
Article in International Conference Proceedings Book
Year
2011
Authors
Ramos, JA
(Author)
Other
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P. Lopes dos Santos
(Author)
FEUP
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Conference proceedings International
Pages: 1491-1496
50th IEEE Conference of Decision and Control (CDC)/European Control Conference (ECC)
Orlando, FL, DEC 12-15, 2011
Indexing
Publicação em ISI Web of Science ISI Web of Science
Scientific classification
FOS: Engineering and technology > Electrical engineering, Electronic engineering, Information engineering
CORDIS: Technological sciences > Engineering > Control engineering
Other information
Authenticus ID: P-008-3RH
Abstract (EN): The fitting of a causal dynamic model to an image is a fundamental problem in image processing, pattern recognition, and computer vision. There are numerous other applications that require a causal dynamic model, such as in scene analysis, machined parts inspection, and biometric analysis, to name only a few. There are many types of causal dynamic models that have been proposed in the literature, among which the autoregressive moving average (ARMA) and state-space models are the most widely known. In this paper we introduce a 2-D stochastic state-space system identification algorithm for obtaining stochastic 2-D, causal, recursive, and separable-in-denominator (CRSD) models in the Roesser state-space form. The algorithm is tested with a real image and the reconstructed image is shown to be almost indistinguishable to the true image.
Language: English
Type (Professor's evaluation): Scientific
No. of pages: 6
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