Hierarchical tensor approximation of multi-dimensional visual data.
IEEE Trans Vis Comput Graph
; 14(1): 186-99, 2008.
Article
em En
| MEDLINE
| ID: mdl-17993712
ABSTRACT
Visual data comprise of multi-scale and inhomogeneous signals. In this paper, we exploit these characteristics and develop a compact data representation technique based on a hierarchical tensor-based transformation. In this technique, an original multi-dimensional dataset is transformed into a hierarchy of signals to expose its multi-scale structures. The signal at each level of the hierarchy is further divided into a number of smaller tensors to expose its spatially inhomogeneous structures. These smaller tensors are further transformed and pruned using a tensor approximation technique. Our hierarchical tensor approximation supports progressive transmission and partial decompression. Experimental results indicate that our technique can achieve higher compression ratios and quality than previous methods, including wavelet transforms, wavelet packet transforms, and single-level tensor approximation. We have successfully applied our technique to multiple tasks involving multi-dimensional visual data, including medical and scientific data visualization, data-driven rendering and texture synthesis.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Gráficos por Computador
/
Reconhecimento Automatizado de Padrão
/
Inteligência Artificial
/
Interpretação de Imagem Assistida por Computador
/
Aumento da Imagem
/
Imageamento Tridimensional
/
Imagem de Difusão por Ressonância Magnética
Tipo de estudo:
Diagnostic_studies
Limite:
Humans
Idioma:
En
Revista:
IEEE Trans Vis Comput Graph
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2008
Tipo de documento:
Article
País de afiliação:
Estados Unidos