基于GAC-CV混合模型的憎水性图像分割算法研究

几何主动轮廓(GAC)模型根据曲线的几何特性可以避免演化过程中重新参数化,但其分割模糊边界对象的效果不佳,而Chan-Vese(CV)模型通过最大化目标与背景的灰度差可以有效地区分图像的模糊边界。基于此,提出一种GAC-CV混合模型,即将图像的边缘信息与区域信息融合进入同一个"能量"泛函,并对不同的分割目标采取不同的分割策略,提高凹形边缘的捕获能力。对绝缘子7种等级的憎水性图像的分割结果表明,该混合模型具有优越的分割性能,对水珠亮点的检测率高达95%。

Investigation on Segmentation Algorithm of Hydrophobicity Images Based on GAC-CV Hybrid Model

The geometric active contour(GAC) model can avoids reparameterization based on the geometry characteristics, but it has the poorly ability in dividing a fuzzy boundary.In contrast, by maximizing the grayscale difference between the target and the background, the Chan-Vese(CV) model can effectively differentiate the fuzzy boundary.Based on the aforementioned consideration, the GAC-CV hybrid model is proposed, where the edge of the image and the region information are merged into the same "energy" function, and the various segmentation strategies are adopted for different segmentation targets to improve the capture ability of the concave edge.The segmentation results about thewater-repellent images of seven grade of the insulators show that the hybrid model has superior segmentation performance, and the detection rate of water bead highlights is as high as 95%.