Anzeige
Erschienen in:
2005 | OriginalPaper | Buchkapitel
Improving ASM Search Using Mixture Models for Grey-Level Profiles
verfasst von : Yanong Zhu, Mark Fisher, Reyer Zwiggelaar
Erschienen in: Pattern Recognition and Image Analysis
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
The use of Active Shape Models (ASM) has been shown to be an efficient approach to image interpretation and pattern recognition. In ASM, grey-level profiles at landmarks are modelled as a Gaussian distribution. Mahalanobis distance from a sample profile to the model mean is used to locate the best position of a given landmark during ASM search. We present an improved ASM methodology, in which the profiles are modelled as a mixture of Gaussians, and the probability that a sample is from the distribution is calculated using the probability density function (pdf) of the mixture model. Both improved and original ASM methods were tested on synthetic and real data. The performance comparison demonstrates that the improved ASM method is more generic and robust than the original approach.