Feature Representation of Natural Images Based on Contours

戴芳 1; 2; 郑南宁 1; 薛建儒 1

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Feature Representation of Natural Images Based on Contours

更新时间：2025-12-08

- Feature Representation of Natural Images Based on Contours
- Vol. 42, Issue 4, Pages: 385-388+394(2008)
- 作者机构：
  
  1. 西安交通大学人工智能与机器人研究所,西安,710049
  2. 西安理工大学理学院,西安,710054
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP391.41
- Online First：10 April 2008，
  
  Published：2008
- 稿件说明：
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戴芳 1, 2, 郑南宁 1, et al. Feature Representation of Natural Images Based on Contours[J]. 2008, 42(4): 385-388+394.
DOI：

戴芳 1, 2, 郑南宁 1, et al. Feature Representation of Natural Images Based on Contours[J]. 2008, 42(4): 385-388+394. DOI：

摘要

结合多尺度图像分解技术和等高线理论提出了一种图像特征表达方法.该方法的独特之处在于将经验模态分解技术应用于图像的梯度模

而不是直接应用于图像本身.对梯度模图像进行经验模态分解

获得表示图像不同尺度下变化信息的固有模态函数

取携带丰富图像特征的前两个固有模态函数进行叠加

对叠加后的固有模态函数求取等高线

以实现对图像特征的提取和表达.实验结果表明

这种图像特征表达方法

不仅可以捕获图像中不同灰度变化属性信息

而且可以获得图像的几何结构

对图像的弱特征信息也有较好的表示能力.

Abstract

An approach of image feature representation that combines the multi-scale image decomposition technique and contour theory is proposed. Its advantage lies in the fact that the empirical mode decomposition technique is applied to the gradient magnitude of the image

not to the image directly. The gradient magnitude image is decomposed by the empirical mode decomposition technique to obtain a number of intrinsic mode functions that capture the gray level change information under different scale of the image. In order to represent the features of the image perfectly

the first two intrinsic mode functions are summed and the results are described by contours. The experiment results show that the proposed representation method for image features not only captures the gray level change information under different scale of image

but also catches the geometric structure information of image

and the weak features of an image can be expressed better.

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references

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HAN Chunming, GUO Huadong, WANG Changlin,et al. A new multiscale edge detection technique [C]∥IEEE International Geoscience and Remote Sensing Symposium. Piscataway, USA: IEEE, 2002: 3402-3404.

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NUNES J C, GUYOT S, DELECHELLE E. Texture analysis based on local analysis of bidimensional empirical mode decomposition [J]. Machine Vision and Application, 2005, 16(3): 177-188.

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