多阈值S-F的光照不均图像分割

Uneven Illumination Image Segmentation Based on Multi-threshold S-F

针对钢管识别统计系统开发中,图像分割环节易受光照不均匀影响的问题,以及对图像增强处理后再分割导致目标错分的不足,本文提出一种多阈值S-F(分割-融合)的图像分割方法。该方法根据改进的Otsu多阈值法,采用形态学操作与图像融合技术,实现堆垛钢管目标的提取。实验结果表明,在光照不均匀情况下,该方法对钢管图像的分割效果明显优于传统方法,具有不受光照优劣程度影响、适应性强的特点,可应用于机器视觉领域的目标识别。

For the problem that image segmentation link of steel tube recognition and counting system is affected easily by uneven illumination, and deficiency that some objects’ mistaken segmentation is caused by segmentation after image’s enhancing, a multi-threshold S-F （Segmentation-Fusion） image segmentation method is proposed. According to improved Otsu multi-threshold method, the morphology algorithm and image fusion technology are applied to extract steel tube objects. Experimental results show that the steel tube image segmentation effect of this method is obviously superior to traditional methods’ under uneven illumination circumstance, and the proposed method is free of the effect of illumination quality and well-adapted, which can be applied in objects recognition of the machine vision field.