J4 ›› 2011, Vol. 38 ›› Issue (3): 164-168+188.doi: 10.3969/j.issn.1001-2400.2011.03.027

• 研究论文 • 上一篇    下一篇



  1. (1. 西北工业大学 自动化学院,陕西 西安  710072|
    2. 西安工业大学 计算机科学与工程学院,陕西 西安  710032)
  • 收稿日期:2010-10-30 出版日期:2011-06-20 发布日期:2011-07-14
  • 通讯作者: 杜亚勤
  • 作者简介:杜亚勤(1972-),女,副教授,西北工业大学博士研究生,E-mail: duyaqin@xatu.edu.cn.
  • 基金资助:


Novel curve fitting edge feature extraction algorithm

DU Yaqin1;HONG Bo2;GUO Lei1;YANG Ning1   

  1. (1. Dept. of Automatic Control, Northwestern Polytechnical Univ., Xi'an   710072, China|
    2. Computer Science and Eng. College, Xi'an Technological Univ., Xi'an   710032, China)
  • Received:2010-10-30 Online:2011-06-20 Published:2011-07-14
  • Contact: DU Yaqin



关键词: 模糊集, 曲面拟合, 边缘检测, 最小二乘支持向量机, 图像处理


The edge contains much visual information of the image, so the image feature extraction is important in image processing. In this paper, the former least squares support vector machines(LS-SVM) edge feature extraction algorithm is analysed, and it is found that its universality is weaken. So this paper proposes a novel method for edge extraction, in which firstly the digital image is transfered to the fuzzy characteristic plane, where the image edge part is extruded, and the other part is weakened. The the image intensity surface is well fitted by the LS-SVM function, in which the first and second derivatives are calculated. Finally, the rather fine image edge feature can be gained. Experiments show that this algorithm can lead to a higher segmentation quality and that the parameters can be fixed, which is very useful in image processing.

Key words: fuzzy sets, curve fitting, edge detection, least squares support vector machines (LS-SVM), image processing


  • TP391.4