支持向量机和元胞自动机相结合的图像边缘检测方法

赵雪峰; 殷国富; 尹湘云; 仲晓敏

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支持向量机和元胞自动机相结合的图像边缘检测方法

电气工程 | 更新时间：2023-11-14

- 支持向量机和元胞自动机相结合的图像边缘检测方法
- Image Edge Detection Based on Support Vector Machine and Cellular Automata
- 工程科学与技术 2011年43卷第1期页码：137-142
- 作者机构：
  
  1. 四川大学制造科学与工程学院
  2. 淮海工学院计算机工程学院
- 作者简介：
- 基金信息：
  
  国家863计划项目（编号：2006AA04Z108），成都市科技公关计划项目（编号：08GGZD089GX-007）
- DOI：
  中图分类号：
- 纸质出版日期：2011，
  
  网络出版日期：2010-2-4，
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赵雪峰,殷国富,尹湘云,仲晓敏.支持向量机和元胞自动机相结合的图像边缘检测方法[J].工程科学与技术,2011,43(1):137-142.

Zhao Xue-Feng, Yin Guo-fu. Image Edge Detection Based on Support Vector Machine and Cellular Automata[J]. Advanced Engineering Sciences, 2011,43(1):137-142.
赵雪峰,殷国富,尹湘云,仲晓敏.支持向量机和元胞自动机相结合的图像边缘检测方法[J].工程科学与技术,2011,43(1):137-142. DOI：

Zhao Xue-Feng, Yin Guo-fu. Image Edge Detection Based on Support Vector Machine and Cellular Automata[J]. Advanced Engineering Sciences, 2011,43(1):137-142. DOI：

摘要

中文摘要: 针对如何提高图像边缘检测效率的问题，提出一种结合最小二乘支持向量机（LSSVM）和元胞自动机进行图像边缘检测的方法。首先，基于Gauss径向基核和多项式核构建出新的核函数，使得LSSVM对图像像素邻域的灰度值能够进行准确的曲面拟合。接着推导出图像的梯度算子，并与图像灰度值进行卷积得到图像的梯度值。然后，元胞自动机按照所设计的局部规则对梯度值进行演化，实现图像边缘的定位和检测。仿真实验检测出的图像边缘定位准确，而且达到一个像素宽，表明新提出的边缘检测算法是有效的；同时，通过对比分析得知新算法具有比Sobel和Canny算法更高的检测性能。

Abstract

Abstract:Aiming at how to establish the ideal standard for the edge detection

a new image edge detection method was proposed based on a combination of least squares support vector machine (LSSVM) and cellular automata. Polynomial and Gaussian kernel function was deployed to construct a new kind of kernel function. LSSVM selected the new kernel function and fitted the image intensity surface for the neighborhood of every pixel. The gradient operators which were deduced from the above LSSVM convoluted with the image gray values to get the image gradient values. Gradient values were evolved out by cellular automata with the designed local rules in order to achieve the best edge detection performance. Simulation results showed that edges were a pixel width and edge positioning was accurate. As illustrated that the proposed algorithm was feasible. Furthermore

the proposed algorithm was higher than the Sobel and the Canny algorithm in detection performance.

关键词

最小二乘支持向量机元胞自动机边缘检测拟合演化规则

Keywords

least squares support vector machinecellular automataedge detectionfittingevolution rules

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