改进的自适应Canny边缘检测算法

传统Canny边缘检测容易受到环境噪声和光照条件变化等外界因素的影响,导致检测效果不佳,而且需要人工选择参数,算法的灵活性较低。为了提高传统算法的检测效果,使其更好的应用到图像识别等领域中,通过最大类间方差法来获得图像梯度量的最佳分割阈值,动态确定边缘检测的高低阈值参数,能够有效解决传统算法的不足。将改进后的算法用于家用仪表计度轮图像的边缘检测中,与传统算法相比,既去除了噪声干扰,又保留了完整的图像边缘,在取得了良好的检测效果的基础上提高了算法效率。

The improved adaptive Canny edge detection algorithm

Traditional Canny edge detection is vulnerable to external factors such as ambient noise and light conditions change, which leads to the bad detection effect, and because of needing artificial parameters, the algorithm has low flexibility. In order to improve the detection effect of the traditional algorithm and make better applications to the field of image recognition and so on, a method using the most between-cluster variance method to get the best threshold for image gradient segmentation, can determine the high and low threshold parameters for edge detection dynamically, solve the shortage of the traditional algorithm effectively. The improved algorithm is used to household instrument counter wheel image edge detection, which wipe off the noise, and keep a complete image edge, and improved the efficiency on the basic of achieving good results.