华南理工大学学报（自然科学版）2025，Vol.53Issue(3)：57-67,11.DOI:10.12141/j.issn.1000-565X.240290
基于多粒度特征-区域关系的赤足足迹分割方法
Segmentation Method of Barefoot Footprint Based on Multi-Granularity Feature and Region Relationship
摘要
Abstract
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 segmenta-tion 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 bare-foot 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 inter-section 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.关键词
图像分割/赤足足迹/多粒度特征/区域关系Key words
image segmentation/barefoot footprint/multi-granularity feature/regional relationship分类
信息技术与安全科学引用本文复制引用
张艳,严毅,吴红英,汪思彤,吴晔峰,王年..基于多粒度特征-区域关系的赤足足迹分割方法[J].华南理工大学学报（自然科学版）,2025,53(3):57-67,11.基金项目
安徽省重点研发计划项目(2022k07020006) （2022k07020006）
安徽省高校自然科学研究重大项目(KJ2021ZD0004) （KJ2021ZD0004）
安徽省高校协同创新项目(GXXT-2022-038) （GXXT-2022-038）
合肥市自然科学基金项目(202303) Supported by the Key R&D Program of Anhui Province(2022k07020006),the University Natural Science Research Major Program of Anhui Province(KJ2021ZD0004)and the University Collaborative Innovation Program of Anhui Pro-vince(GXXT-2022-038) （202303）
