采用语义分割方法自动分割赤足足迹图像时，虽然可以减少人工干预，但对于赤足足迹图像分割中脚趾区域模糊的问题，神经网络模型需要更加重视这些区域的特征提取；对于光照不均匀的赤足足迹图像，可以通过模型建立赤足整体区域与局部区域的上下文关系，利用整体区域特征信息来增强光照不均匀区域的特征表达，以提升图像分割的准确性与稳健性。为此，该文提出了基于多粒度特征-区域关系的赤足足迹分割方法，通过局部区域标签使特征表示关注脚趾区域，提取足迹的多粒度特征，并与足迹的全局特征进行融合，以提升对赤足足迹中模糊区域的分割效果；同时，对原始图像和足迹特征图进行空间变换，采用矩阵相乘建立两者间赤足区域关系矩阵，利用关系矩阵对赤足全局特征进行空间调制，以实现特征增强。该文还构建了一个现场赤足足迹数据集（包含25人的1 100幅现场赤足足迹图像），并针对模糊、光照不均、模糊-光照不均和正常4种赤足足迹图像进行实验。结果表明，在正常赤足足迹图像上分割时，赤足类交并比达到93.50%，在模糊、光照不均、模糊-光照不均3类赤足足迹图像上分割时，赤足类交并比分别达到92.90%、93.06%、91.66%，而在模糊-光照不均赤足足迹图像上分割的赤足类交并比相比于U-Net提升了1.15个百分点。

When using semantic segmentation methods to automatically segment barefoot footprint images, although manual intervention can be reduced, the issue of blurred toe regions in barefoot footprint image segmentation requires the neural network model to pay more attention to feature extraction from these areas. For barefoot footprint images with uneven lighting, the model can establish contextual relationships between the global and local regions of the footprint, using the feature information from the global region to enhance the feature expression of the uneven lighting areas, thereby improving the accuracy and robustness of image segmentation. To address this, this paper proposed a barefoot footprint segmentation method based on multi-granularity feature-region relationships. By using local region labels, the method enhances feature representation in the toe area, extracts multi-granularity features of footprints, and integrates them with global footprint features to improve segmentation performance in blurred areas. Meanwhile, spatial transformations were applied to both the original image and the footprint feature map, and a matrix multiplication approach was used to establish a barefoot region relationship matrix between them. This relationship matrix was then utilized to spatially modulate the global barefoot features, achieving feature enhancement. Furthermore, this paper constructed an in-the-wild barefoot footprint dataset consisting of 1 100 barefoot footprint images from 25 individuals and conducted experiments on four types of barefoot footprint images: blurred, unevenly illuminated, both blurred and unevenly illuminated, and normal. The results show that the intersection over union (IoU) for the barefoot class reaches 93.50% on normal barefoot footprint images. For blurred, uneven lighting, and blurry-uneven lighting images, the IoU are 92.90%, 93.06%, and 91.66%, respectively. Notably, the IoU for blurry-uneven lighting images is improved by 1.15 percentage points compared to U-Net.