基于SLIC显著性检测及C-V模型的紫外图像分割研究

为实现紫外图像中高压设备放电区域的自动分割,该文提出一种基于SLIC显著性检测及C-V主动轮廓模型的图像分割方法。首先将紫外图像进行SLIC超像素分割,然后进行显著性检测,最后将显著性检测结果输入C-V模型进行图像分割。实验结果表明,与传统的C-V模型和RSF&LoG模型相比,该文方法不仅可以准确区分图像的前景(放电区域)和背景,从而准确提取紫外图像放电区域,而且具有良好的抗噪性能,取得最好的紫外分割图像结果。

In order to realize the automatic segmentation of the discharge area of high-voltage equipment in the ultraviolet image, this paper proposes an image segmentation method based on SLIC saliency detection and C-V active contour model. First, the ultraviolet image is segmented by SLIC super pixel, then the saliency detection is performed, and finally the saliency detection result is input into the C-V model for image segmentation. The experimental results show that compared with the traditional C-V model and RSF&LoG model, the method in this paper can not only accurately distinguish the foreground(discharge area) and background of the image, so as to accurately extract the discharge area of the ultraviolet image, but also has good anti-noise performance, achieving the best ultraviolet segmentation image results.