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结构张量,计算方向程序,是的方向性更强,更精确-Structure tensor
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结构张量 在图像处理中的应用 图像的去噪 图像的向量-Structure tensor in the application of image processing the image de-noising the image vector
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用于张量计算的数据结构，一些简单的操作实现-Tensor calculated for the data structure, simple operation to achieve
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系统地分析了各向异性扩散方法的理论基础，并提出了边缘保留与基于结构张量的各向异性扩散算法-Systematic analysis of the theoretical basis of anisotropic diffusion method, and proposed to retain the edge of the structure tensor based anisotropic diffusion algorithm
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本文献讲述了使用多维的结构张量进行基于分水岭的图像增强和去噪方法-The literature describes the use of multi-dimensional structure of tensor-based watershed image enhancement and denoising
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该程序是Weickert提出的结构张量扩散去噪模型。-The program corresponds to the structure tensor diffusion denoising model which is proposed by Weickert
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matlab程序语言，图像增强算法的代码- Function [U, minv, SS] = Nonlinear_Diffusion (U_0, tau, eps, p, T, theta, sigma, fig_handle)
Performs nonlinear scalar valued and coupled vector/matrix valued diffusion
Inputs:
U_0 nxmx dxw input field:
D = 1, w
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利用非线性结构张量处理一些图形问题，能够有效的解决和优化现成的处理方法-In this article, we introduce nonlinear versions of the popular structure tensor, also known as second moment matrix. These nonlinear structure
tensors replace the Gaussian smoothing of the classical structure te
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图像融合技术，较其他现成的技术更为先进，能够解决从非线性结构张量计算，更为逼真-A novel image fusion method based on nonlinear
structure tensor is proposed within the wavelet framework. Local
geometric information in detail wavelet sub-band is extracted and
recognized by structure te
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Harris角点检测算法的研究，主要从梯度和结构张量两方面的编程-Harris corner detection algorithm research, mainly from the gradient and programming structure tensor both
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根据结构张量能区分图像的平坦区域、边缘区域与角点区域.-Based on structure tensor can distinguish between the image smooth area, edge area and angular point area.
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代码支持齐次线性和非线性（总变差和边缘增强流动）的任意尺寸领域各向同性扩散（标量/灰度图像，彩色图像和矩阵向量/结构张量）。添加剂算子分裂（AOS）以及高斯正则化的实现加速计算。-The code supports homogeneous and linear and nonlinear (Total Variation and Edge Enhancing flow) isotropic diffusion of arbitrary dimensioned fields(scalar~gray
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张量分解提取生物学特征，NFEA: Tensor Toolbox for Feature
Extraction and Applications-
Data in modern applications such as BCI based on EEG signals often contain multi-modes due to
mechanism of data recording, e.g. signals recorded by multiple-sensors (elec
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