用于红外眼动图像分割的优化方法

Optimization Method for Infrared Eye Movement Image Segmentation

眼动仪采集红外眼动数据时受试者眼球快速运动或与仪器无法保持相对静止,导致采集的部分采集眼区图像出现虚焦、模糊,针对该问题,提出了一种语义分割优化系统,即超实时语义分割网络(S-RITnet)。首先,制作训练集、验证集、测试集图像数量比例为4∶1∶1的像素级标注数据集。然后,用增强超分辨率生成对抗网络和限制对比度的自适应直方图增强算法修复模糊眼区的数据集图像。最后,基于实时语义分割网络和自主数据集(含修复数据集)进行训练,实现对眼区图像的语义分割,并对获得的分割模块进行评价。实验结果表明,该优化方案可以有效优化眼区图像质量,相比低质量的眼区图像训练模块,S-RITnet的平均交并比提升了0.0247,F1分数提升了0.024。

When the eye tracker is collecting infrared eye movement data,due to the rapid movement of the subject’s eyeballs or the inability to keep relatively still with the instrument,some of the collected eye area images are defocused and blurred.This paper proposes a semantic segmentation optimization system,which is called super real-time semantic segmentation network(S-RITnet).First,a pixel-level annotation data set with a4∶1∶1 ratio of images in the training set,validation set,and test set is created.Then,the enhance superresolution generative adversarial network and contrast-limited adaptive histogram enhancement algorithm are used to repair the blurred eye area data set image.Finally,based on real-time semantic segmentation net and the autonomous data set(including the repair data set),perform network training to realize the semantic segmentation of the eye area image and evaluate the obtained segmentation module.The experimental results show that the optimization scheme can effectively optimize the quality of eye area images.Compared with the low-quality eye images training module,the mean intersection over union and F1-score evaluation of S-RITnet increased by0.0247 and 0.024 respectively.