全方位空间自闭症儿童眼动信号处理与分析

针对以往自闭症（autism spectrum disorder， ASD）眼动研究无法展现真实全方位世界中自闭症患者的视觉注意的问题，本文提出应用虚拟现实（virtual reality， VR）技术和360°全景图像，在VR环境下收集眼动和头动数据，以分析和模拟自闭症儿童在真实全方位世界中的视觉注意。文中建立了首个大规模的全景图像ASD眼动数据集，改进了三层显著性计算模型的所考虑图像特征及其提取方法，量化了数据集中所有全景图像的特征，使用支持向量机（support vector machine， SVM）计算了不同特征对视觉注意力分配的影响程度。进而，我们从对图像中不同特征的视觉关注度，到头眼运动的区别与联系，进行了对自闭症儿童与对照组视觉注意的定性与定量比较，得到了自闭症儿童非典型视觉注意的特征。这项研究有助于分析自闭症的视觉特征，可以进一步帮助辅助自闭症的分类、诊断和预后康复。 

Processing and Analysis of Eye Movement Signals for Autistic Children in Omnidirectional Space

? ?To address the problem that previous eye-movement studies for autism spectrum disorder （ASD） cannot show the visual attention of autistic patients in the real omnidirectional world， in this paper， we propose to utilize virtual reality （VR） technology and omnidirectional 360 degree images to collect eye movement data and head movement data in VR environment， and then further analyze and simulate the visual attention of autistic children in the real omnidirectional world. Specifically， we first establish a large-scale panoramic image eye-movement dataset for autistic children. Then， based on the constructed dataset， we improve the extracted features and methods in the three-layer saliency calculation model. We quantify the features of all panoramic images of the dataset， and calculate the influence of different features on visual attention via support vector machine （SVM）. Finally， based on the collected data and computational results， we qualitatively and quantitatively analyze the correlation and difference between the visual attention of autistic children and typically developing controls including the visual preference for different features and head-eye movement correlations. This study can contribute to analyzing the visual characteristics of autism and can further assist the procedure of classification， diagnosis and rehabilitation of autism.