[an error occurred while processing this directive]
���¿��ټ��� �߼�����
   ��ҳ  �ڿ�����  ��ί��  Ͷ��ָ��  �ڿ�����  ��������  �� �� ��  ��ϵ����
�������պ����ѧѧ�� 2008, Vol. 34 Issue (09) :1012-1015    DOI:
���� ����Ŀ¼ | ����Ŀ¼ | ������� | �߼����� << | >>
���Ӵ�1, ����ˬ1, ��Ծ��2, Ҧ����3*
1. �������պ����ѧ �Զ�����ѧ���������ѧԺ, ���� 100191;
2. �й����ʺ��չɷ����޹�˾���̼����ֹ�˾ �ɶ�ά�޻���, �ɶ� 610200;
3. �й����ú����ܾ� ���հ�ȫ��������, ���� 100028
Application of improved PCA to thermal wave image processing
Sun Yanchun1, Ma Qishuang1, Liu Yueming2, Yao Hongyu3*
1. School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China;
2. Chengdu Aircraft Maintenance Base of Air China Limited, Engineering Technics Branch, Chengdu 610200, China;
3. Center of Aviation Safety Technology, Civil Aviation Administration of China, Beijing 100028, China

Download: PDF (349KB)   HTML 1KB   Export: BibTeX or EndNote (RIS)      Supporting Info
ժҪ ��Էɻ��ṹ���ظ�ʴ�Ȳ��������г��ֵ����������������ݽ��١�ͼ�������и�ʴ������Ǹ�ʴ����ԱȲ����ԡ�����Ƚϵ͵�����,����˻���С���任�ĸĽ�PCA(Principal Component Analysis)�㷨.���㷨����ͼ�������е�ÿ֡ͼ����ж�������ɢС���任,��ȡ����Ƶ���ֵ�ϵ��;�Ե�Ƶϵ�����иĽ�PCA����,ͨ���̶����㷨������Gram-Schmidt���������������������;���Ƶϵ��Ϊ��,����ͼ�������.ʵ��������:���㷨��������������ǵ���������,�����ͼ��������������,�Ҽ���ʱ��϶�.
Email Alert
�ؼ����� ����   ͼ����   ����������(PCA)   С���任     
Abstract�� To solve the problems less valid data in thermal infrared imager, uneasy dipartite corrosion area in image and lower the signal to noise ratio which happened in detection of structure parts- hidden corrosion by thermal wave imaging, the improved PCA (principal component analysis) based on the wavelet was presented. The every frame image was transformed by dyadic discrete wavelet and the characteristic of wavelet transform coefficient was analyzed. Low frequency coefficients were analyzed by fast principal component method which quoted Gram-Schmidt orthonormalization procedure in fixed-point algorithm then distilled principal component and reconstructed the low frequency coefficients. To suppress image noise, the high frequency coefficients were equated to zero. The experimental results indicated that the method made best of thermal infrared imager-s dada, improved the contrast of infrared thermal image and reduced noise of infrared thermal image, and that the method-s compute-time was short.
Keywords�� thermography   image processing   principal component analysis(PCA)   wavelet transforms     
Received 2007-08-06;


About author: ���Ӵ�(1978-),��,ɽ��������,��ʿ��,ace9024@126.com.
���Ӵ�,����ˬ,��Ծ��,Ҧ����.�Ľ�PCA���Ȳ�ͼ�����е�Ӧ��[J]  �������պ����ѧѧ��, 2008,V34(09): 1012-1015
Sun Yanchun, Ma Qishuang, Liu Yueming,Yao Hongyu.Application of improved PCA to thermal wave image processing[J]  JOURNAL OF BEIJING UNIVERSITY OF AERONAUTICS AND A, 2008,V34(09): 1012-1015
http://bhxb.buaa.edu.cn//CN/     ��     http://bhxb.buaa.edu.cn//CN/Y2008/V34/I09/1012
Copyright 2010 by �������պ����ѧѧ��