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

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2011

Autores

Ramos, JA (Autor) Outra; P. Lopes dos Santos (Autor) FEUP

Ata de Conferência Internacional

Título: 2011 50TH IEEE CONFERENCE ON DECISION AND CONTROL AND EUROPEAN CONTROL CONFERENCE (CDC-ECC)

Páginas: 1491-1496

50th IEEE Conference of Decision and Control (CDC)/European Control Conference (ECC)

Orlando, FL, DEC 12-15, 2011

Indexação

ISI Web of Knowledge - 2 Citações

ISI Web of Science

Scopus - 3 Citações

Classificação Científica

FOS: Ciências da engenharia e tecnologias > Engenharia electrotécnica, electrónica e informática

CORDIS: Ciências Tecnológicas > Engenharia > Engenharia de controlo

Outras Informações

ID Authenticus: P-008-3RH

DOI: 10.1109/cdc.2011.6161291

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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6