Edge and texture detection of metal image under high temperature and dynamic solidification condition

The zinc casting is a complicated process with high temperature, high dust content and dynamic solidification. To accurately detect the edge and texture of metal image under this condition, a sub-pixel detection based on gradient entropy and adaptive four-order cubic convolution interpolation （GEAF-CCI） algorithm is proposed. This method mainly involves three procedures. Firstly, the gradient image is generated from the grey images by using gradient operator. Then, a dynamic threshold based on the maximum local gradient entropy （DTMLGE） algorithm is applied to distinguishing the edge and texture pixels from gradient images. Finally, the adaptive four-order cubic convolution interpolation （AF-CCI） algorithm is proposed for interpolating calculation of the target edges and textures according to their variation differences in different directions. The experimental result shows that the proposed algorithm can remove the jag and blur of the edges and textures, improve the edge positioning precision and reduce the false or missing detection rate.

锌锭铸造是一个高温、高粉尘和动态凝固的复杂过程。为了准确地检测该条件下金属图像的边缘和纹理特征,提出了一种基于梯度熵和自适应四阶立方卷积插值的亚像素检测算法(GEAF-CCI)。该方法主要包含3个过程:首先,采用梯度算子从灰度图像中生成梯度图像;然后,采用基于最大局部梯度熵的动态阈值(DTMLGE)算法去区分梯度图像中的边缘和纹理的像素;最后,使用AF-CCI算法根据目标边缘和纹理在不同方向的变化差异对其进行插值计算。实验结果表明,该算法可以减少细节模糊和边缘锯齿现象的产生,提高边缘的定位精度和降低误检率和失检率。