基于Sobel算子的工件圆弧轮廓特征提取

Feature Extraction of Workpiece Circular Arc Contour Based on Sobel Operator

在双目视觉工件圆弧半径测量过程中,圆弧轮廓特征提取是后续边缘轮廓点匹配及空间圆弧重构的关键。受工件表面纹理、周围环境干扰、光照不均匀等影响,现有算法并不能准确提取圆弧轮廓特征。在Sobel算子进行边缘检测的基础上,利用自适应卷积运算和双局部二值模式纹理特征进行归一化处理产生融合灰度值,进而利用融合灰度值对Sobel算子检测结果进一步筛选出边缘轮廓点。对极坐标分布直方图进行一般正态分布处理,排除背景复杂时噪声点的干扰,进一步区分外轮廓特征和内轮廓特征。实验结果表明,本文算法不仅消除了光照影响,而且具有很好的准确性和稳健性。

In the process of workpiece circular arc radius measurement on binocular vision, the circular arc contour feature extraction is the key to match follow-up edge contour point and reconstruct space arc. Affected by the surface texture of the workpiece, the interference of the surrounding environment, and uneven illumination, the existing algorithm cannot accurately extract the circular arc contour feature. Based on the edge detection by Sobel operator, we use adaptive convolution operation and double local binary pattern texture feature to generate the fusion gray value, which can be used to screen out the edge contour points in the previous detection result by Sobel operator. The polar coordinate distribution histogram is obtained and processed as a normal distribution, which can eliminate the interference of noise points in the complex background and distinguish the outer contour feature from the inner contour feature. The experimental results show that the proposed algorithm overcomes the influence of illumination and becomes accurate and robust.