Combined morphological-spectral unsupervised image segmentation.
IEEE Trans Image Process
; 14(1): 49-62, 2005 Jan.
Article
em En
| MEDLINE
| ID: mdl-15646872
ABSTRACT
The goal of segmentation is to partition an image into disjoint regions, in a manner consistent with human perception of the content. For unsupervised segmentation of general images, however, there is the competing requirement not to make prior assumptions about the scene. Here, a two-stage method for general image segmentation is proposed, which is capable of processing both textured and nontextured objects in a meaningful fashion. The first stage extracts texture features from the subbands of the dual-tree complex wavelet transform. Oriented median filtering is employed, to circumvent the problem of texture feature response at step edges in the image. From the processed feature images, a perceptual gradient function is synthesised, whose watershed transform provides an initial segmentation. The second stage of the algorithm groups together these primitive regions into meaningful objects. To achieve this, a novel spectral clustering technique is proposed, which introduces the weighted mean cut cost function for graph partitioning. The ability of the proposed algorithm to generalize across a variety of image types is demonstrated.
Buscar no Google
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Gráficos por Computador
/
Processamento de Sinais Assistido por Computador
/
Reconhecimento Automatizado de Padrão
/
Inteligência Artificial
/
Interpretação de Imagem Assistida por Computador
/
Aumento da Imagem
Tipo de estudo:
Diagnostic_studies
/
Evaluation_studies
Idioma:
En
Ano de publicação:
2005
Tipo de documento:
Article