Manifold learning for object tracking with multiple nonlinear models.
IEEE Trans Image Process
; 23(4): 1593-605, 2014 Apr.
Article
em En
| MEDLINE
| ID: mdl-24577194
ABSTRACT
This paper presents a novel manifold learning algorithm for high-dimensional data sets. The scope of the application focuses on the problem of motion tracking in video sequences. The framework presented is twofold. First, it is assumed that the samples are time ordered, providing valuable information that is not presented in the current methodologies. Second, the manifold topology comprises multiple charts, which contrasts to the most current methods that assume one single chart, being overly restrictive. The proposed algorithm, Gaussian process multiple local models (GP-MLM), can deal with arbitrary manifold topology by decomposing the manifold into multiple local models that are probabilistic combined using Gaussian process regression. In addition, the paper presents a multiple filter architecture where standard filtering techniques are integrated within the GP-MLM. The proposed approach exhibits comparable performance of state-of-the-art trackers, namely multiple model data association and deep belief networks, and compares favorably with Gaussian process latent variable models. Extensive experiments are presented using real video data, including a publicly available database of lip sequences and left ventricle ultrasound images, in which the GP-MLM achieves state of the art results.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Processamento de Imagem Assistida por Computador
/
Inteligência Artificial
/
Dinâmica não Linear
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
IEEE Trans Image Process
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2014
Tipo de documento:
Article