基于模糊梯度的微齿轮图像边缘检测算法

The Edge Detection Algorithm of Micro-Gear Image Based on Fuzzy Gradient Theory

  • 摘要: 提出一种基于模糊梯度思想的微齿轮图像边缘检测算法. 该算法采用正切归一化函数对灰度图像模糊化,并计算该模糊图像的梯度矩阵;采用正弦函数对梯度矩阵进一步模糊化得到模糊梯度矩阵;采用基于归一化实数编码的遗传算法来搜索最优的阈值λ对模糊梯度矩阵做截集,获取图像边缘点集合. 实验中以不同微齿轮图像作为实验对象,测试了该算法性能. 实验结果表明,该算法的检测精度优于10 μs,检测时间基本为1 s,整体性能优于Canny算法和Pal-King算法.

     

    Abstract: A new image edge detection algorithm which is based on the theory of fuzzy gradient is proposed in the paper. The steps of the algorithm is as follows: firstly, the original gray image is changed into fuzzy matrix by the normalized tangent function; secondly, gradient matrix of the fuzzy matrix mentioned above is computed; thirdly, the fuzzy gradient matrix which is also called fuzzy set of the gradient matrix is obtained using sine function; and in the end, appropriate threshold which is identified by genetic algorithm based on normalized real-coded is used to segment the fuzzy gradient set into two subsets: the edge point set and non-edge point set. At this point, the image edge points are obtained. To test the speed and accuracy of the algorithm for detecting edge of micro-part image, the experiments have been done taking different kinds of micro-gear as the subjects. The experimental results show that the detecting accuracy is better than 10 μs and the processing time is generally about 1 s. Compared with Canny or Pal-King algorithms, it could be concluded that the performance of proposed new algorithm is the best.

     

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