Doping inhomogeneity and staging of ultra-thin graphite intercalation compound flakes probed by visible and near-infrared Raman spectroscopy

When ultra-thin graphite intercalation compounds（GICs） are deposited on the SiO2/Si substrate, it is found that their colors are dependent on the thickness of GIC flakes. The sample colors of ultrathin GIC flakes can no longer provide qualitative information on the stage index. Here, multi-wavelength Raman spectroscopy is thus applied to study the doping inhomogeneity and staging of ultra-thin GICs by FeCl3 intercalation. The G band intensity of stage-1 GIC flakes is strongly enhanced by 532-nm laser excitation, while that of stage-2 and stage-3 flakes exhibits strong intensity enhancement for 785-nm laser excitation. The near-infrared lasers are suggested to probe the doping inhomogeneity and staging of ultra-thin GIC flakes.

A Fuzzy Cluster and Retinex Theory-based Variation Model for Inhomogenous Image Segmentation

A variational inhomogeneous image segmentation model based on fuzzy membership functions and Retinex theory is proposed by introducing the fuzzy membership function.The existence of the solution of the proposed model is proved theoretically.A valid algorithm is designed to make numerical solution of the model under the framework of alternating minimization.The last experimental results show that the model can make segmentation of the real image with intensity inhomogeneity effectively.

A Novel Active Contour Model for Medical Image Segmentation

A novel segmentation method for medical image with intensity inhomogeneity is introduced.In the proposed active contour model,both region and gradient information are taken into consideration.The former,i.e.,region-based fitting energy,draws upon the region information and guarantees the accurate extraction of inhomogeneous image's local information.The latter,i.e.,an edge indicator,weights the length penalizing term to consider the gradient constrain.Moreover,signed distance penalizing term is also added to ensure accurate computation and avoid the time-consuming re-initialization of evolving level set function.Experiments for synthetic and real images demonstrate the feasibility and superiority of the proposed model.

MRI image segmentation based on fast kernel clustering analysis

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.