北京航空航天大学学报 ›› 2006, Vol. 32 ›› Issue (08): 954-957.

• 论文 • 上一篇    下一篇

基于Gabor滤波器的图像目标识别方法

曾姝彦, 张广军, 李秀智   

  1. 北京航空航天大学 仪器科学与光电工程学院, 北京 100083
  • 收稿日期:2005-10-10 出版日期:2006-08-31 发布日期:2010-09-19
  • 作者简介:曾姝彦(1982-),女,湖北洪湖人,硕士生,zengshuyan@126.com.
  • 基金资助:

    国家自然科学基金资助项目(50375012)

Image target distinguish based on Gabor filters

Zeng Shuyan, Zhang Guangjun, Li Xiuzhi   

  1. School of Instrument Science and Opto-electronics Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China
  • Received:2005-10-10 Online:2006-08-31 Published:2010-09-19

摘要: 为了给机器人视觉导航提供有效信息,提出一种基于图像匹配的目标识别方法.对CCD采集的目标图像,由 Gabor 变换生成的二维Gabor 滤波器有着优良的滤波器性能,不需要对图像进行分割,能适应一定的旋转、尺度、光照的变化,通过多个频率和角度的Gabor算子与图像的卷积,获取图像全局信息的特征描述.分类方法采用统计学习理论基础上发展起来的一种新的机器学习方法——支持向量机(SVM, Support Vector Machines),它可解决模型选择、过学习、维数灾难等问题.通过支持向量机进行多维特征向量的分类.该方法可达到较高的识别率,达到实时处理的要求,可以在人脸识别、机器人视觉定位等领域得到应用.

Abstract: In order to provide useful information for mobile robot, the target distinguish approach based on image matching needs to recognize image pattern. 2D Gabor filters were created by Gabor transform, which had excellent performance and didn′t need to segmente images catched by the single CCD camera. Gabor filters were robust to the various orientations and illumination of target images; They also satisfied the real time image processing. The multi-channel Gabor filters, with multi-frequencies and multi-angles, were used to convolute with images. The corresponding filter results included entirely information of images, from which the feature vectors of the images could be extracted. The method of classifying these feature vectors was SVM (support vector machine). SVM was a new method of machine learning developed by statistical learning theory. It resolved problems such as model select, over learning, non-linear, high dimension, etc. Experiment results indicate that the algorithm can reach up to high recognize rate. The algorithm can be applied in fields such as face recognition, robot vision localization and so on.

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