针对土壤染色图像色度不一致、染色/非染色区域对比度低的特点,以及现有土壤染色图像分割方法自动化程度弱的问题,该研究提出一种土壤优先流自动分割与量化系统。该系统采用基于H分量改进的模糊C均值方法(Fuzzy C-Means Based on H Comp...针对土壤染色图像色度不一致、染色/非染色区域对比度低的特点,以及现有土壤染色图像分割方法自动化程度弱的问题,该研究提出一种土壤优先流自动分割与量化系统。该系统采用基于H分量改进的模糊C均值方法(Fuzzy C-Means Based on H Component and Morphology,HM-FCM)实现染色区域的自动分割,运用数学统计法提取总染色面积比、基质流深度、优先流比等特征参数,实现对土壤染色区域的量化分析,以揭示优先流的发育程度。并基于2种林地染色图像验证了系统性能。试验结果表明:1)HM-FCM法对于天然次生林和榛子林图像均具有最佳分割效果,其分割准确率为87.9%和83.3%,调和平均值为90.5%和80.3%;2)2种林地土壤染色区域总体集中于0~50 cm土层,优先流具有不同发育程度(P<0.05)。该系统可为优先流路径的空间演变提供技术支持和理论依据。展开更多
In situ microscopic imaging is a useful tool in monitoring crystallization processes,including crystal nucleation,growth,aggregation and breakage,as well as possible polymorphic transition.To convert the qualitative i...In situ microscopic imaging is a useful tool in monitoring crystallization processes,including crystal nucleation,growth,aggregation and breakage,as well as possible polymorphic transition.To convert the qualitative information to be quantitative for the purpose of process optimization and control,accurate analysis of crystal images is essential.However,the accuracy of image segmentation with traditional methods is largely affected by many factors,including solid concentration and image quality.In this study,the deep learning technique using mask region-based convolutional neural network(Mask R-CNN)is investigated for the analysis of on-line images from an industrial crystallizer of 10 m^(3) operated in continuous mode with high solid concentration and overlapped particles.With detailed label points for each crystal and transfer learning technique,two models trained with 70,908 and 7,709 crystals respectively are compared for the effect of training data amount.The former model effectively segments the aggregated and overlapped crystals even at high solid concentrations.Moreover,it performs much better than the latter one and traditional multi-scale method both in terms of precision and recall,revealing the importance of large number of crystals in deep learning.Some geometrical characteristics of segmented crystals are also analyzed,involving equivalent diameter,circularity,and aspect ratio.展开更多
文摘针对土壤染色图像色度不一致、染色/非染色区域对比度低的特点,以及现有土壤染色图像分割方法自动化程度弱的问题,该研究提出一种土壤优先流自动分割与量化系统。该系统采用基于H分量改进的模糊C均值方法(Fuzzy C-Means Based on H Component and Morphology,HM-FCM)实现染色区域的自动分割,运用数学统计法提取总染色面积比、基质流深度、优先流比等特征参数,实现对土壤染色区域的量化分析,以揭示优先流的发育程度。并基于2种林地染色图像验证了系统性能。试验结果表明:1)HM-FCM法对于天然次生林和榛子林图像均具有最佳分割效果,其分割准确率为87.9%和83.3%,调和平均值为90.5%和80.3%;2)2种林地土壤染色区域总体集中于0~50 cm土层,优先流具有不同发育程度(P<0.05)。该系统可为优先流路径的空间演变提供技术支持和理论依据。
基金Financial support from the National Natural Science Foundation of China(grant No.61633006)is acknowledged。
文摘In situ microscopic imaging is a useful tool in monitoring crystallization processes,including crystal nucleation,growth,aggregation and breakage,as well as possible polymorphic transition.To convert the qualitative information to be quantitative for the purpose of process optimization and control,accurate analysis of crystal images is essential.However,the accuracy of image segmentation with traditional methods is largely affected by many factors,including solid concentration and image quality.In this study,the deep learning technique using mask region-based convolutional neural network(Mask R-CNN)is investigated for the analysis of on-line images from an industrial crystallizer of 10 m^(3) operated in continuous mode with high solid concentration and overlapped particles.With detailed label points for each crystal and transfer learning technique,two models trained with 70,908 and 7,709 crystals respectively are compared for the effect of training data amount.The former model effectively segments the aggregated and overlapped crystals even at high solid concentrations.Moreover,it performs much better than the latter one and traditional multi-scale method both in terms of precision and recall,revealing the importance of large number of crystals in deep learning.Some geometrical characteristics of segmented crystals are also analyzed,involving equivalent diameter,circularity,and aspect ratio.
