.png){kind=icon}
返回
TEXSOM: Texture segmentation using self-organizing maps
.png){kind=icon}
PRE
AI
DOI：10.1016/S0925-2312(98)00041-1.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This article describes the so-called TEXSOM-architecture, a texture segmentation architecture based on the joint spatial/spatial-frequency paradigm. In this architecture the oriented filters are automatically generated using the adaptive-subspace self-organizing map (ASSOM) or the supervised ASSOM (SASSOM) neural models. The automatic filter generation overcomes some drawbacks of similar architectures, such as the large size of the filter bank and the necessity of a priori knowledge to determine the filters' parameters. The quality of the segmentation process is improved by applying median filtering and the watershed transformation over the pre-segmented images. The proposed architecture is also suitable to perform defect identification on textured images. (C) 1998 Elsevier Science B.V. All rights reserved.
Keyword:
adaptive-subspace self-organizing map (ASSOM)
supervised ASSOM (SASSOM)
joint spatial spatial-frequency analysis methods
gabor filters
texture segmentation
watershed transformation
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
暂无机构信息
引用论文
Avascular Necrosis With Complete Resorption of the Proximal Humerus Following Undisplaced Three-Part Fracture.png)
Orthopedics
OrthopedicsIF0
PREAI
分享收藏
分享收藏分享收藏
查看更多内容