针对经典图像去雾算法在边缘区域易产生光晕效应、天空等明亮区域还原失真、色调偏移等问题,提出一种基于天空检测和超像素分割的改进暗通道图像去雾新方法(Dark Channel Prior based on Sky Detection and Super Pixel,SSPDCP).首先对...针对经典图像去雾算法在边缘区域易产生光晕效应、天空等明亮区域还原失真、色调偏移等问题,提出一种基于天空检测和超像素分割的改进暗通道图像去雾新方法(Dark Channel Prior based on Sky Detection and Super Pixel,SSPDCP).首先对雾图采用HSV变换提取亮度分量进行自适应阈值分割;然后应用图像连通分析技术识别天空域;接着利用天空域估计大气光值,针对天空和非天空区域分别建立各自的透射率计算模型,并基于构建的超像素级透射率融合模型获得融合透射率图,以促进边界区域的平滑过渡,采用多尺度引导滤波精化透射率图;最后应用大气散射模型完成图像复原并进行亮度增强处理,实现无雾图像的自然恢复.该方法识别的天空区域较为连续完整,以超像素代替方形窗口可以有效克服局部块效应的影响,大气光值和透射率图估计更为客观准确.从主观定性和客观定量评价方面来看,该方法复原的图像具有整体误差小、信噪比优良、结构相似度高等优势.本文所提出的图像去雾新方法能有效抑制边缘区域的光晕效应,且复原的天空区域明亮自然,图像去雾质量相比现有方法有进一步提升.展开更多
This study addresses the problem of classifying emotional words based on recorded electroencephalogram （EEG） signals by the single-trial EEG classification technique. Emotional two-character Chinese words are used a...This study addresses the problem of classifying emotional words based on recorded electroencephalogram （EEG） signals by the single-trial EEG classification technique. Emotional two-character Chinese words are used as experimental materials. Positive words versus neutral words and negative words versus neutral words are classified, respectively, using the induced EEG signals. The method of temporally regularized common spatial patterns （TRCSP） is chosen to extract features from the EEG trials, and then single-trial EEG classification is achieved by linear discriminant analysis. Classification accuracies are between 55% and 65%. The statistical significance of the classification accuracies is confirmed by permutation tests, which shows the successful identification of emotional words and neutral ones, and also the ability to identify emotional words. In addition, 10 out of 15 subjects obtain significant classification accuracy for negative words versus neutral words while only 4 are significant for positive words versus neutral words, which demonstrate that negative emotions are more easily identified.展开更多
As a result of the recently increasing demands on high-performance aero-engine,the machining accuracy of blade profile is becoming more stringent. However,in the current profile,precision milling,grinding or near-nets...As a result of the recently increasing demands on high-performance aero-engine,the machining accuracy of blade profile is becoming more stringent. However,in the current profile,precision milling,grinding or near-netshape technology has to undergo a tedious iterative error compensation. Thus,if the profile error area and boundary can be determined automatically and quickly,it will help to improve the efficiency of subsequent re-machining correction process. To this end,an error boundary intersection approach is presented aiming at the error area determination of complex profile,including the phaseⅠof cross sectional non-rigid registration based on the minimum error area and the phaseⅡof boundary identification based on triangular meshes intersection. Some practical cases are given to demonstrate the effectiveness and superiority of the proposed approach.展开更多
文摘针对经典图像去雾算法在边缘区域易产生光晕效应、天空等明亮区域还原失真、色调偏移等问题,提出一种基于天空检测和超像素分割的改进暗通道图像去雾新方法(Dark Channel Prior based on Sky Detection and Super Pixel,SSPDCP).首先对雾图采用HSV变换提取亮度分量进行自适应阈值分割;然后应用图像连通分析技术识别天空域;接着利用天空域估计大气光值,针对天空和非天空区域分别建立各自的透射率计算模型,并基于构建的超像素级透射率融合模型获得融合透射率图,以促进边界区域的平滑过渡,采用多尺度引导滤波精化透射率图;最后应用大气散射模型完成图像复原并进行亮度增强处理,实现无雾图像的自然恢复.该方法识别的天空区域较为连续完整,以超像素代替方形窗口可以有效克服局部块效应的影响,大气光值和透射率图估计更为客观准确.从主观定性和客观定量评价方面来看,该方法复原的图像具有整体误差小、信噪比优良、结构相似度高等优势.本文所提出的图像去雾新方法能有效抑制边缘区域的光晕效应,且复原的天空区域明亮自然,图像去雾质量相比现有方法有进一步提升.
基金The National Natural Science Foundation of China(No.61375118)the Program for New Century Excellent Talents in University of China(No.NCET-12-0115)
文摘This study addresses the problem of classifying emotional words based on recorded electroencephalogram （EEG） signals by the single-trial EEG classification technique. Emotional two-character Chinese words are used as experimental materials. Positive words versus neutral words and negative words versus neutral words are classified, respectively, using the induced EEG signals. The method of temporally regularized common spatial patterns （TRCSP） is chosen to extract features from the EEG trials, and then single-trial EEG classification is achieved by linear discriminant analysis. Classification accuracies are between 55% and 65%. The statistical significance of the classification accuracies is confirmed by permutation tests, which shows the successful identification of emotional words and neutral ones, and also the ability to identify emotional words. In addition, 10 out of 15 subjects obtain significant classification accuracy for negative words versus neutral words while only 4 are significant for positive words versus neutral words, which demonstrate that negative emotions are more easily identified.
基金supported by the Aeronautical Science Foundation of China (No.20200016112001)。
文摘As a result of the recently increasing demands on high-performance aero-engine,the machining accuracy of blade profile is becoming more stringent. However,in the current profile,precision milling,grinding or near-netshape technology has to undergo a tedious iterative error compensation. Thus,if the profile error area and boundary can be determined automatically and quickly,it will help to improve the efficiency of subsequent re-machining correction process. To this end,an error boundary intersection approach is presented aiming at the error area determination of complex profile,including the phaseⅠof cross sectional non-rigid registration based on the minimum error area and the phaseⅡof boundary identification based on triangular meshes intersection. Some practical cases are given to demonstrate the effectiveness and superiority of the proposed approach.
