The study of the grain-size distribution of gravels is always an important and challenging issue in stratigraphy and morphology, especially in the field of automated measurement. It largely reduces many manual process...The study of the grain-size distribution of gravels is always an important and challenging issue in stratigraphy and morphology, especially in the field of automated measurement. It largely reduces many manual processes and time consumption. Precise segmentation method plays a very important role in it. In this study, a digital image method using an improved normalized cuts algorithm is proposed for auto-segmentation of gravel image. It added grain-size estimation, and used the feature vector based on color. It has made great improvements in many respects, especially in accuracy of edge segmentation and automation. Compared with manual measurement methods and other image processing methods, the method studied in this paper is an efficient method for precisely segmenting gravel images.展开更多
基于视频的非接触光电容积脉搏波(Photoplethysmography,PPG)可以实现非接触式心率监测。为改善非接触PPG信号质量和提高非接触PPG技术检测心率的准确性,提出一种自适应感兴趣区域(Region of Interest,ROI)的方法。使用独立向量分析对...基于视频的非接触光电容积脉搏波(Photoplethysmography,PPG)可以实现非接触式心率监测。为改善非接触PPG信号质量和提高非接触PPG技术检测心率的准确性,提出一种自适应感兴趣区域(Region of Interest,ROI)的方法。使用独立向量分析对人脸分区域处理,然后使用归一化分割选取信噪比和相关度最高的小区块作为自适应ROI来获取心率,通过对自适应ROI加权平均和频域处理得到非接触PPG信号。相比于预选定ROI的方法,该方法将头部静止状态下心率误差的均值和标准差从(4.72±6.46)次/分降低至(0.52±1.49)次/分,根均方误差(Root Mean Square Error,RMSE)从7.96次/分降低至1.50次/分,平均误差率从9.45%降低至1.73%。头部运动状态下该方法的误差为(1.02±2.91)次/分,RMSE为2.11次/分,误差降低50%以上。使用Bland-Altman及相关性分析比较该方法与使用接触式PPG仪器得到的心率,计算得到头部静止时95%置信区间为-2.44~3.48次/分,运动时为-2.76~4.79次/分。最后通过对比与接触式PPG信号的波形,证明该方法得到了细节完整的PPG信号。实验结果表明,该方法显著提升了PPG信号的质量与心率的准确率。展开更多
文摘The study of the grain-size distribution of gravels is always an important and challenging issue in stratigraphy and morphology, especially in the field of automated measurement. It largely reduces many manual processes and time consumption. Precise segmentation method plays a very important role in it. In this study, a digital image method using an improved normalized cuts algorithm is proposed for auto-segmentation of gravel image. It added grain-size estimation, and used the feature vector based on color. It has made great improvements in many respects, especially in accuracy of edge segmentation and automation. Compared with manual measurement methods and other image processing methods, the method studied in this paper is an efficient method for precisely segmenting gravel images.
文摘基于视频的非接触光电容积脉搏波(Photoplethysmography,PPG)可以实现非接触式心率监测。为改善非接触PPG信号质量和提高非接触PPG技术检测心率的准确性,提出一种自适应感兴趣区域(Region of Interest,ROI)的方法。使用独立向量分析对人脸分区域处理,然后使用归一化分割选取信噪比和相关度最高的小区块作为自适应ROI来获取心率,通过对自适应ROI加权平均和频域处理得到非接触PPG信号。相比于预选定ROI的方法,该方法将头部静止状态下心率误差的均值和标准差从(4.72±6.46)次/分降低至(0.52±1.49)次/分,根均方误差(Root Mean Square Error,RMSE)从7.96次/分降低至1.50次/分,平均误差率从9.45%降低至1.73%。头部运动状态下该方法的误差为(1.02±2.91)次/分,RMSE为2.11次/分,误差降低50%以上。使用Bland-Altman及相关性分析比较该方法与使用接触式PPG仪器得到的心率,计算得到头部静止时95%置信区间为-2.44~3.48次/分,运动时为-2.76~4.79次/分。最后通过对比与接触式PPG信号的波形,证明该方法得到了细节完整的PPG信号。实验结果表明,该方法显著提升了PPG信号的质量与心率的准确率。
