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http://dx.doi.org/10.3745/KIPSTB.2003.10B.3.311

Multiple Texture Objects Extraction with Self-organizing Optimal Gabor-filter  

Lee, Woo-Beom (대구과학대학 컴퓨터공학과)
Kim, Wook-Hyun (영남대학교 전자정보공학부)
Publication Information
The KIPS Transactions:PartB / v.10B, no.3, 2003 , pp. 311-320 More about this Journal
Abstract
The Optimal filter yielding optimal texture feature separation is a most effective technique for extracting the texture objects from multiple textures images. But, most optimal filter design approaches are restricted to the issue of supervised problems. No full-unsupervised method is based on the recognition of texture objects in image. We propose a novel approach that uses unsupervised learning schemes for efficient texture image analysis, and the band-pass feature of Gabor-filter is used for the optimal filter design. In our approach, the self-organizing neural network for multiple texture image identification is based on block-based clustering. The optimal frequency of Gabor-filter is turned to the optimal frequency of the distinct texture in frequency domain by analyzing the spatial frequency. In order to show the performance of the designed filters, after we have attempted to build a various texture images. The texture objects extraction is achieved by using the designed Gabor-filter. Our experimental results show that the performance of the system is very successful.
Keywords
Unsupervised clustering; Self-organizing neural network; Optimal Frequency Analysis; Optimal Gabor-filter; Texture objects Extraction;
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Times Cited By KSCI : 2  (Citation Analysis)
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