A scheme for an automatic road surface modeling from a noisy point cloud is presented. The normal vectors of the point cloud are estimated by distance-weighted fitting of local plane. Then, an automatic recognition of...A scheme for an automatic road surface modeling from a noisy point cloud is presented. The normal vectors of the point cloud are estimated by distance-weighted fitting of local plane. Then, an automatic recognition of the road surface from noise is performed based on the fuzzy clustering of normal vectors, with which the mean value is calculated and the projecting plane of point cloud is created to obtain the geometric model accordingly. Based on fuzzy clustering of the intensity attributed to each point, different objects on the road surface are assigned different colors for representing abundant appearances. This unsupervised method is demonstrated in the experiment and shows great effectiveness in reconstructing and rendering better road surface.展开更多
Glaucoma is an eye disease that usually occurs with the increased Intra-Ocular Pressure(IOP),which damages the vision of eyes.So,detecting and classifying Glaucoma is an important and demanding task in recent days.For...Glaucoma is an eye disease that usually occurs with the increased Intra-Ocular Pressure(IOP),which damages the vision of eyes.So,detecting and classifying Glaucoma is an important and demanding task in recent days.For this purpose,some of the clustering and segmentation techniques are proposed in the existing works.But,it has some drawbacks that include ineficient,inaccurate and estimates only the affected area.In order to solve these issues,a Neighboring Differential Clustering(NDC)-Intensity V ariation Making(IVM)are proposed in this paper.The main intention of this work is to extract and diagnose the abnormal retinal image by identifying the optic disc.This work includes three stages such as,preprocessing,clustering and segmentation.At first,the given retinal image is preprocessed by using the Gaussian Mask Updated(GMU)model for eliminating the noise and improving the quality of the image.Then,the cluster is formed by extracting the threshold and patterns with the help of NDC technique.In the segmentation stage,the weight is calculated for pixel matching and ROI extraction by using the proposed IVM method.Here,the novelty is presented in the clustering and segmentation processes by developing NDC and IVM algorithms for accurate Glaucoma identification.In experiments,the results of both existing and proposed techniques are evaluated in terms of sensitivity,specificity,accuracy,Hausdorff distance,Jaccard and dice metrics.展开更多
分析总结了ADHoc网络经典的分群算法,将ADHoc网络的分群归纳为四个阶段,并提出了一种新的基于信号强度的分群方法SWCA(Weighted Clustering Algorithm based on signal strength)。该方法利用了节点检测到的信号强度来分群和维护群结构...分析总结了ADHoc网络经典的分群算法,将ADHoc网络的分群归纳为四个阶段,并提出了一种新的基于信号强度的分群方法SWCA(Weighted Clustering Algorithm based on signal strength)。该方法利用了节点检测到的信号强度来分群和维护群结构,并较好的解决了障碍物导致网络连通性的问题。它涉及信号强度权值的计算、节点稳定性的判断、节点的剩余电池能量三个方面的问题。本文描述了该算法的模型,以及它与WCA算法相比存在的优点。展开更多
Kernel-based clustering is supposed to provide a better analysis tool for pattern classification,which implicitly maps input samples to a highdimensional space for improving pattern separability.For this implicit spac...Kernel-based clustering is supposed to provide a better analysis tool for pattern classification,which implicitly maps input samples to a highdimensional space for improving pattern separability.For this implicit space map,the kernel trick is believed to elegantly tackle the problem of“curse of dimensionality”,which has actually been more challenging for kernel-based clustering in terms of computational complexity and classification accuracy,which traditional kernelized algorithms cannot effectively deal with.In this paper,we propose a novel kernel clustering algorithm,called KFCM-III,for this problem by replacing the traditional isotropic Gaussian kernel with the anisotropic kernel formulated by Mahalanobis distance.Moreover,a reduced-set represented kernelized center has been employed for reducing the computational complexity of KFCM-I algorithm and circumventing the model deficiency of KFCM-II algorithm.The proposed KFCMIII has been evaluated for segmenting magnetic resonance imaging(MRI)images.For this task,an image intensity inhomogeneity correction is employed during image segmentation process.With a scheme called preclassification,the proposed intensity correction scheme could further speed up image segmentation.The experimental results on public image data show the superiorities of KFCM-III.展开更多
基金Supported by the National Natural Science Foundation of China （No.40471089） and the Key Laboratory of Geo-informatics of State Bureau of Surveying and Mapping.
文摘A scheme for an automatic road surface modeling from a noisy point cloud is presented. The normal vectors of the point cloud are estimated by distance-weighted fitting of local plane. Then, an automatic recognition of the road surface from noise is performed based on the fuzzy clustering of normal vectors, with which the mean value is calculated and the projecting plane of point cloud is created to obtain the geometric model accordingly. Based on fuzzy clustering of the intensity attributed to each point, different objects on the road surface are assigned different colors for representing abundant appearances. This unsupervised method is demonstrated in the experiment and shows great effectiveness in reconstructing and rendering better road surface.
文摘Glaucoma is an eye disease that usually occurs with the increased Intra-Ocular Pressure(IOP),which damages the vision of eyes.So,detecting and classifying Glaucoma is an important and demanding task in recent days.For this purpose,some of the clustering and segmentation techniques are proposed in the existing works.But,it has some drawbacks that include ineficient,inaccurate and estimates only the affected area.In order to solve these issues,a Neighboring Differential Clustering(NDC)-Intensity V ariation Making(IVM)are proposed in this paper.The main intention of this work is to extract and diagnose the abnormal retinal image by identifying the optic disc.This work includes three stages such as,preprocessing,clustering and segmentation.At first,the given retinal image is preprocessed by using the Gaussian Mask Updated(GMU)model for eliminating the noise and improving the quality of the image.Then,the cluster is formed by extracting the threshold and patterns with the help of NDC technique.In the segmentation stage,the weight is calculated for pixel matching and ROI extraction by using the proposed IVM method.Here,the novelty is presented in the clustering and segmentation processes by developing NDC and IVM algorithms for accurate Glaucoma identification.In experiments,the results of both existing and proposed techniques are evaluated in terms of sensitivity,specificity,accuracy,Hausdorff distance,Jaccard and dice metrics.
文摘分析总结了ADHoc网络经典的分群算法,将ADHoc网络的分群归纳为四个阶段,并提出了一种新的基于信号强度的分群方法SWCA(Weighted Clustering Algorithm based on signal strength)。该方法利用了节点检测到的信号强度来分群和维护群结构,并较好的解决了障碍物导致网络连通性的问题。它涉及信号强度权值的计算、节点稳定性的判断、节点的剩余电池能量三个方面的问题。本文描述了该算法的模型,以及它与WCA算法相比存在的优点。
基金This work was partially supported by the National Natural Science Foundation of China(Grant Nos.60872145,60902063)the National High Technology Research and Development Program of China(Grant No.2009AA01Z315)+1 种基金the Cultivation Fund of the Key Scientific and Technical Innovation Project,Ministry of Education of China(No.708085)the Henan Research Program of Foundation and Advanced Technology(No.082300410090).
文摘Kernel-based clustering is supposed to provide a better analysis tool for pattern classification,which implicitly maps input samples to a highdimensional space for improving pattern separability.For this implicit space map,the kernel trick is believed to elegantly tackle the problem of“curse of dimensionality”,which has actually been more challenging for kernel-based clustering in terms of computational complexity and classification accuracy,which traditional kernelized algorithms cannot effectively deal with.In this paper,we propose a novel kernel clustering algorithm,called KFCM-III,for this problem by replacing the traditional isotropic Gaussian kernel with the anisotropic kernel formulated by Mahalanobis distance.Moreover,a reduced-set represented kernelized center has been employed for reducing the computational complexity of KFCM-I algorithm and circumventing the model deficiency of KFCM-II algorithm.The proposed KFCMIII has been evaluated for segmenting magnetic resonance imaging(MRI)images.For this task,an image intensity inhomogeneity correction is employed during image segmentation process.With a scheme called preclassification,the proposed intensity correction scheme could further speed up image segmentation.The experimental results on public image data show the superiorities of KFCM-III.
