Unsupervised Texture Image Segmentation Based on Gabor Wavelet and Multi-PCNN

Author

Wang, Minqin ; Han, Guoqiang ; Tu, Yongqiu ; Chen, Guohua ; Gao, Yuefang

Author_Institution

Sch. of Comput. Sci. & Eng., South China Univ. of Technol., Guangzhou

Volume

2

fYear

2008

fDate

20-22 Dec. 2008

Firstpage

376

Lastpage

381

Abstract

This paper is a research on unsupervised texture segmentation technique using Gabor filters and multi-PCNN. The partitioning method based on Gabor filters gets the good segmentation result, but causes the huge data. PCNN is a parallel way and can be easily realized with hardware, especially VLSI, which makes PCNN process image fast. We present an algorithm which uses Gabor filters to extract image texture character which has been inputted into PCNNs to segment the image. This method can get good segmentation result and improve the algorithm´s processing speed.

Keywords

Gabor filters; feature extraction; image segmentation; image texture; neural nets; wavelet transforms; Gabor filter; Gabor wavelet transform; feature extraction; image partitioning method; multiPCNN; pulse coupled neural network; unsupervised texture image segmentation; Application software; Computer science; Data mining; Feature extraction; Frequency; Gabor filters; Hardware; Image segmentation; Information technology; Software engineering; Gabor filter; PCNN; feature extration; texture segmentation;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Information Technology Application, 2008. IITA '08. Second International Symposium on

Conference_Location

Shanghai

Print_ISBN

978-0-7695-3497-8

Type

conf

DOI

10.1109/IITA.2008.294

Filename

4739790