|Table of Contents|

[1] Xiao Hua, Shu Huazhong, Yu Wenxue, Li Songyi, et al. Orthogonal moment based texture segmentation [J]. Journal of Southeast University (English Edition), 2003, 19 (1): 31-34. [doi:10.3969/j.issn.1003-7985.2003.01.008]
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Orthogonal moment based texture segmentation()
基于正交矩的纹理分割
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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
19
Issue:
2003 1
Page:
31-34
Research Field:
Computer Science and Engineering
Publishing date:
2003-03-30

Info

Title:
Orthogonal moment based texture segmentation
基于正交矩的纹理分割
Author(s):
Xiao Hua Shu Huazhong Yu Wenxue Li Songyi
Department of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
肖华 舒华忠 於文雪 李松毅
东南大学生物科学与医学工程系, 南京 210096
Keywords:
Legendre moment texture radial basis function neural network
勒让得矩 纹理 RBF人工神经网络
PACS:
TP391.4
DOI:
10.3969/j.issn.1003-7985.2003.01.008
Abstract:
Texture segmentation is a necessary step to identify the surface or an object in an image. We present a Legendre moment based segmentation algorithm. The Legendre moments in small local windows of the image are computed first and a nonlinear transducer is used to map the moments to texture features and these features are used to construct feature vectors used as input data. Then an RBF neural network is used to perform segmentation. A k-mean algorithm is used to train the hidden layers of the RBF neural network. The training of the output layer is the supervised algorithm based on LMS. The algorithm has been successfully used to segment a number of gray level texture images. Compared with the geometric moment-based texture segmentation, we can reduce the error rates using orthogonal moments.
在识别一幅图像中的界面或者物体时, 一般先要进行纹理分割.本文提出了基于勒让得矩的纹理分割方法.首先在图像的小窗口中计算矩值, 然后用一个非线性转换器把它转化成纹理特征.再用这些特征组成特征向量作为输入数据.接着采用RBF人工神经网络对提取的特征进行分割.用k均值算法训练RBF人工神经网络的隐层.输出层的训练是采用基于LMS的监督式数学模型.该算法成功地分割了许多灰度级图像.和基于几何矩的纹理分割相比, 用正交矩可以降低分割错误率.

References:

[1] Haralick R M. Statistical and structural approaches to texture[J]. Proceedings of the IEEE, 1979, 67(5):786-804.
[2] Davis L S, Clearman M, Aggarwal J K. An empirical evaluation of generalized cooccurrence matrices[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1981, 3(2):214-221.
[3] Gagalowicz A. Blind texture segmentation[A]. In: Proc of the 9th International Conference on Pattern Recognition[C]. Rome, Italy, 1988. 46-50.
[4] Coggins J M, Jain A K. A spatial filtering approach to texture analysis[J]. Pattern Recognition Letters, May, 1985, PRL(3):195-203.
[5] Clark M, Bovik A C, Geisler W S. Texture segmentation using Gabor modulation/demodulation[J]. Pattern Recognition Letters, Sept, 1987, PRL(6):261-267.
[6] Turner M R. Texture discrimination by Gabor functions[J]. Biological Cybernetics, 1986, 55:71-82.
[7] Voorhees H, Poggio T. Detecting textons and texture boundaries in natural images[A]. In: Proc of the First International Conference on Computer Vision[C]. London, 1987. 250-258.
[8] Mihran Tuceryan. Moment based texture segmentation[J]. Pattern Recognition Letters, July, 1994, PRL(15):659-668.
[9] Caelli T, Oguztoreli M N. Some tasks and signal dependent rules for spatial vision[J]. Spatial Vision, 1987, 2:295-315.
[10] Moody J, Darken C J. Fast learning in networks of locally-tuned processing units[J]. Neural Computation, 1989, 1: 281-293.

Memo

Memo:
Biographies: Xiao Hua(1978—), female, graduate; Shu Huazhong(corresponding author), male, professor, shu.list@seu.edu.cn.
Last Update: 2003-03-20