霍夫变换耦合蚁群优化图像边缘提取算法

目的为解决图像边缘提取方法中由于噪声浸染导致边缘定位精确度降低、边缘信息丢失和虚假边缘等不足,提出基于霍夫变换(HT)耦合蚁群优化(ACO)图像边缘的提取方法。方法对输入图像进行霍夫变换,消除噪声和线段间隔对图像边缘的影响;计算图像像素梯度和像素圆形邻域统计均值的差值,构建二者之间的权重函数,并作为蚁群的信息素和启发信息;利用蚁群优化算法,引导蚁群搜索图像边缘,完成图像边缘提取。结果实验表明,与当前边缘提取技术相比,文中算法具有更高的提取精度与效率,可获取完整、细节丰富的边缘,有效地降低了噪声影响。结论所提算法具有较强的抗噪性能,能进一步改善边缘提取精度,能够较好地用于包装条码识别与图像处理领域。

The Image Edge Extraction Algorithm Based on Hough Transform Coupling Ant Colony Optimization

The work aims to propose an image edge extraction method based on Hough transform (HT) coupling ant colony optimization (ACO), in order to solve the reduced edge positioning accuracy, edge information loss and false edge, etc. in the image edge extraction method caused by the noise. Firstly, HT was carried out for input image to eliminate the effect of noise and line segment on image edge. Secondly, the difference between the pixel gradient of the image and the statistical average of the circular neighborhood was calculated, and the weight function between the two was constructed and used as the pheromone and the heuristic information of ant colony. Finally, the ant colony optimization algorithm was used to guide the ant colony to search image, so as to finish the image edge extraction. The experiment results showed that, compared with the current edge detection technique, the proposed algorithm had higher extraction accuracy and efficiency, and could obtain complete edges with abundant details, which effectively reduced the noise effect. In conclusion, the proposed algorithm has a stronger anti-noise performance and can further improve the edge extraction accuracy. Because of that, it can be better applied in the field of package barcode recognition and image processing.