[an error occurred while processing this directive]
   
 
���¿��ټ��� �߼�����
   ��ҳ  �ڿ�����  ��ί��  Ͷ��ָ��  �ڿ�����  ��������  �� �� ��  ��ϵ����
�������պ����ѧѧ�� 2008, Vol. 34 Issue (05) :572-575    DOI:
���� ����Ŀ¼ | ����Ŀ¼ | ������� | �߼����� << | >>
����С�����ֽ��FCM���������ͼ��ָ��
����, Ԭ����*
�������պ����ѧ ������Ϣ����ѧԺ, ����100083
Texture image segmentation method based on wavelet packet transform and FCM clustring
Wu Yang, Yuan Yunneng*
School of Electronics and Information Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China

ժҪ
�����
�������
Download: PDF (379KB)   HTML 1KB   Export: BibTeX or EndNote (RIS)      Supporting Info
ժҪ �����һ���µ�ͼ��������ȡ��ѡȡ����С���ֽ����ķ���.��ʽС���ֽ���źŽⲻ��ȫ��,��С����ȫ�ֽ��������Ӵ�ļ�����,���С���ֽ���������ѡȡ��Ϊ��Ҫ.���ģ��c��ֵ(FCM,Fuzzy C-Mean)����,�����һ����ͬʱ����С������Ӧ�ֽ�������������������ͼ��ָ��,�÷������޼ල�����еľ�����Ч�Բ������뵽����ӦС���ֽ���о���,�ܸ����޼ල����ָ����Ҫ,����Ӧ��ѡȡС�����ֽ�����νṹ�ͷֽ����.�����С����ȫ�ֽ�,��ʡ�˴���������,����ȡ�����õķָ�Ч��.
Service
�ѱ����Ƽ�������
�����ҵ����
�������ù�����
Email Alert
RSS
�����������
����
Ԭ����
�ؼ����� ͼ��ָ�   С���任   ģ��c��ֵ����   ����С����     
Abstract�� A new method of optimal tree structure selection of wavelet transformation for image segmentation was presented. The standard pyramid-structure wavelet transform founded on the same recursive technique: only the low-pass outputs were used. It could not adjust the decomposition to accurate and efficient texture description. Although the wavelet packet transform provided a much more detailed analysis of the frequency content of a texture, it is often the case that areas which contain little or no frequency information are recursively decomposed. So the selection of optimal wavelet basis for texture characterization is very important. By introducing the validity measure for fuzzy clustering to the decision of wavelet decomposition structure, the presented algorithm simultaneously performs the adaptive wavelet decomposition and the texture feature classification, moreover it adaptively chooses the wavelet decomposition structure and depth. Compared with the wavelet packet decomposition, the algorithm reduces the computational burden, while obtains satisfactory segmentation results.
Keywords�� image segmentation   wavelet transform   fuzzy c-means clustering   optimal wavelet basis     
Received 2007-04-27;
About author: �� ��(1982��),Ů,������,˶ʿ��,eilse@126.com.
���ñ���:   
����, Ԭ����.����С�����ֽ��FCM���������ͼ��ָ��[J]  �������պ����ѧѧ��, 2008,V34(05): 572-575
Wu Yang, Yuan Yunneng.Texture image segmentation method based on wavelet packet transform and FCM clustring[J]  JOURNAL OF BEIJING UNIVERSITY OF AERONAUTICS AND A, 2008,V34(05): 572-575
���ӱ���:  
http://bhxb.buaa.edu.cn//CN/     ��     http://bhxb.buaa.edu.cn//CN/Y2008/V34/I05/572
Copyright 2010 by �������պ����ѧѧ��