It is proposed a high resolution remote sensing image segmentation method which combines static minimum spanning tree(MST)tessellation considering shape information and the RHMRF-FCM algorithm.It solves the problems i...It is proposed a high resolution remote sensing image segmentation method which combines static minimum spanning tree(MST)tessellation considering shape information and the RHMRF-FCM algorithm.It solves the problems in the traditional pixel-based HMRF-FCM algorithm in which poor noise resistance and low precision segmentation in a complex boundary exist.By using the MST model and shape information,the object boundary and geometrical noise can be expressed and reduced respectively.Firstly,the static MST tessellation is employed for dividing the image domain into some sub-regions corresponding to the components of homogeneous regions needed to be segmented.Secondly,based on the tessellation results,the RHMRF model is built,and regulation terms considering the KL information and the information entropy are introduced into the FCM objective function.Finally,the partial differential method and Lagrange function are employed to calculate the parameters of the fuzzy objective function for obtaining the global optimal segmentation results.To verify the robustness and effectiveness of the proposed algorithm,the experiments are carried out with WorldView-3(WV-3)high resolution image.The results from proposed method with different parameters and comparing methods(multi-resolution method and watershed segmentation method in eCognition software)are analyzed qualitatively and quantitatively.展开更多
Hand gesture recognition is a popular topic in computer vision and makes human-computer interaction more flexible and convenient.The representation of hand gestures is critical for recognition.In this paper,we propose...Hand gesture recognition is a popular topic in computer vision and makes human-computer interaction more flexible and convenient.The representation of hand gestures is critical for recognition.In this paper,we propose a new method to measure the similarity between hand gestures and exploit it for hand gesture recognition.The depth maps of hand gestures captured via the Kinect sensors are used in our method,where the 3D hand shapes can be segmented from the cluttered backgrounds.To extract the pattern of salient 3D shape features,we propose a new descriptor-3D Shape Context,for 3D hand gesture representation.The 3D Shape Context information of each 3D point is obtained in multiple scales because both local shape context and global shape distribution are necessary for recognition.The description of all the 3D points constructs the hand gesture representation,and hand gesture recognition is explored via dynamic time warping algorithm.Extensive experiments are conducted on multiple benchmark datasets.The experimental results verify that the proposed method is robust to noise,articulated variations,and rigid transformations.Our method outperforms state-of-the-art methods in the comparisons of accuracy and efficiency.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
基金National Natural Science Foundation of China(No.41271435)National Natural Science Foundation of China Youth Found(No.41301479)。
文摘It is proposed a high resolution remote sensing image segmentation method which combines static minimum spanning tree(MST)tessellation considering shape information and the RHMRF-FCM algorithm.It solves the problems in the traditional pixel-based HMRF-FCM algorithm in which poor noise resistance and low precision segmentation in a complex boundary exist.By using the MST model and shape information,the object boundary and geometrical noise can be expressed and reduced respectively.Firstly,the static MST tessellation is employed for dividing the image domain into some sub-regions corresponding to the components of homogeneous regions needed to be segmented.Secondly,based on the tessellation results,the RHMRF model is built,and regulation terms considering the KL information and the information entropy are introduced into the FCM objective function.Finally,the partial differential method and Lagrange function are employed to calculate the parameters of the fuzzy objective function for obtaining the global optimal segmentation results.To verify the robustness and effectiveness of the proposed algorithm,the experiments are carried out with WorldView-3(WV-3)high resolution image.The results from proposed method with different parameters and comparing methods(multi-resolution method and watershed segmentation method in eCognition software)are analyzed qualitatively and quantitatively.
基金supported by the National Natural Science Foundation of China(61773272,61976191)the Six Talent Peaks Project of Jiangsu Province,China(XYDXX-053)Suzhou Research Project of Technical Innovation,Jiangsu,China(SYG201711)。
文摘Hand gesture recognition is a popular topic in computer vision and makes human-computer interaction more flexible and convenient.The representation of hand gestures is critical for recognition.In this paper,we propose a new method to measure the similarity between hand gestures and exploit it for hand gesture recognition.The depth maps of hand gestures captured via the Kinect sensors are used in our method,where the 3D hand shapes can be segmented from the cluttered backgrounds.To extract the pattern of salient 3D shape features,we propose a new descriptor-3D Shape Context,for 3D hand gesture representation.The 3D Shape Context information of each 3D point is obtained in multiple scales because both local shape context and global shape distribution are necessary for recognition.The description of all the 3D points constructs the hand gesture representation,and hand gesture recognition is explored via dynamic time warping algorithm.Extensive experiments are conducted on multiple benchmark datasets.The experimental results verify that the proposed method is robust to noise,articulated variations,and rigid transformations.Our method outperforms state-of-the-art methods in the comparisons of accuracy and efficiency.
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.
