Hesitant fuzzy information allows clustering data with multiple possible membership values for a single item in a reference set. Hesitant fuzzy sets have been applied in many decision-making problems, obtaining better results against others kinds of fuzzy sets. So, in this paper a method for image segmentation based on the hesitant fuzzy set theory is investigated. Additionally, processing time is sped up with a hardware-level parallelization technique using OpenMP. Comparing the experimental results, it can be seen that the segmentation by the propose algorithm is superior, compared to some of the state of the art. The most striking feature to emerge from this algorithm is its ability to preserve the details of the boundaries of the region, in addition to the fact that the regions are more homogeneous.

犹豫模糊信息允许对参考集中的单个项目具有多个可能的隶属度值的数据进行聚类。犹豫模糊集已被应用于许多决策问题，相对于其他类型的模糊集获得了更好的结果。因此，本文研究了一种基于犹豫模糊集理论的图像分割方法。此外，使用 OpenMP 的硬件级并行化技术可加快处理时间。比较实验结果，可以看出，与一些现有技术相比，所提出算法的分割效果更好。该算法最显着的特点是它能够保留区域边界的细节，此外还有区域更均匀的事实。