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

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10μs,检测时间基本为1s,整体性能优于Canny算法和Pal-King算法.

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.