为研究儿童对不同形状字体警示语标识的视觉注意特征,将字体形状为单一变量的警示语标识图片作为视觉刺激材料,利用Eye Link II型眼动仪记录了30名小学生观看这些图片时的注视点个数和首次注视时间等试验数据,用SPSS分析了注视点个数和...为研究儿童对不同形状字体警示语标识的视觉注意特征,将字体形状为单一变量的警示语标识图片作为视觉刺激材料,利用Eye Link II型眼动仪记录了30名小学生观看这些图片时的注视点个数和首次注视时间等试验数据,用SPSS分析了注视点个数和首次注视时间,基于视觉注意理论结合主观调查问卷结果表明:1)被试者对黑体字体注意程度最大;2)儿童对不同形状字体警示语标识的注意程度由大到小为黑体、隶书、宋体(楷体)、幼圆。展开更多
In this paper, an approach to predicting randomly-shaped particle volume based on its two- Dimensional （2-D） digital image is explored. Conversion of gray-scale image of the particles to its binary counterpart is fi...In this paper, an approach to predicting randomly-shaped particle volume based on its two- Dimensional （2-D） digital image is explored. Conversion of gray-scale image of the particles to its binary counterpart is first performed using backlighting technique. The silhouette of particle is thus obtained, and consequently, informative features such as particle area, centroid and shape-related descriptors are collected. Several dimensionless parameters are defined, and used as regressor variables in a multiple linear regression model to predict particle volume. Regressor coefficients are found by fitting to a randomly selected sample of 501 panicles ranging in size from 4.75mm to 25ram. The model testing experiment is conducted against a different aggregate sample of the similar statistical properties, the errors of the model-predicted volume of the batch is within ±2%.展开更多
文摘为研究儿童对不同形状字体警示语标识的视觉注意特征,将字体形状为单一变量的警示语标识图片作为视觉刺激材料,利用Eye Link II型眼动仪记录了30名小学生观看这些图片时的注视点个数和首次注视时间等试验数据,用SPSS分析了注视点个数和首次注视时间,基于视觉注意理论结合主观调查问卷结果表明:1)被试者对黑体字体注意程度最大;2)儿童对不同形状字体警示语标识的注意程度由大到小为黑体、隶书、宋体(楷体)、幼圆。
基金Funded by the Zhejiang Provincial Educatrion Ministry (No.2004884), and the Scientific Research Start-up Foundation of Ningbo University (No.2004037).
文摘In this paper, an approach to predicting randomly-shaped particle volume based on its two- Dimensional （2-D） digital image is explored. Conversion of gray-scale image of the particles to its binary counterpart is first performed using backlighting technique. The silhouette of particle is thus obtained, and consequently, informative features such as particle area, centroid and shape-related descriptors are collected. Several dimensionless parameters are defined, and used as regressor variables in a multiple linear regression model to predict particle volume. Regressor coefficients are found by fitting to a randomly selected sample of 501 panicles ranging in size from 4.75mm to 25ram. The model testing experiment is conducted against a different aggregate sample of the similar statistical properties, the errors of the model-predicted volume of the batch is within ±2%.
