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提出了一种新颖的处理图像视觉聚类问题的方法。与以往关注图像局部特征和局部连续性的方法不同，本文中的方法能够提取关于图像的全局印象。为此，我们将图像分割问题转化为图划分问题并提出了划分中的一种全局判别准则——Ncut (Normalized Cut)。Ncut不仅能够衡量不同聚类之间的相异程度，还能够衡量各聚类内部的相似程度。为求解Ncut 的最优化问题，提出了一种基于广义特征值问题的高效算法，并将此算法应用于静态图像分割，取得了良好的效果。-proposed a novel image proc
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Normalized Cut Image Segmentation Code
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自己写的Normalized Cut图像分割，聚类程序，程序非常清晰，花了很长时间调试。-Normalize Cut
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图像处理中的多尺度分割程序，使用matlab开发环境-Image processing procedures for multiscale segmentation
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Data Clustering with Normalized Cuts
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进行normalized cut算法的图像分割-Normalized cut algorithm for image segmentation
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This paper provides an algorithm for partitioning grayscale images into disjoint regions of coherent
brightness and texture. Natural images contain both textured and untextured regions, so the cues of contour and
texture differences are exploited
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多尺度normalized cut分割的matlab代码
-Multiscale Normalized Cuts Segmentation Toolbox
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基于分水岭算法和图论的图像分割，所使用的方法为分水岭算法和Normalized Cut方法-Based on watershed algorithm and graph theory, image segmentation, the method used as a watershed algorithm and Normalized Cut method
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GCUT: segmentation by normalized graph cut as described
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Normalized Cut for image segmentation
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一种基于图像分割和归一化灰度Hough变换的SAR图像舰船尾迹CFAR检测算法-Image segmentation based on gray scale and normalized Hough transform SAR ship wake image CFAR detection algorithm
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五种当前主要的基于图的图像分割方法（normalized cut, min-cut/max-flow, isoperimetric partitioning, minimum spanning tree and random walker）的论文原文。-The original papers of five main graph-based image segmentation methods. They are normalized cut, min-cut/max-flow, isoperi
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Normalized Cuts and Image Segmentation
xp下的32位的绝对可以运行-Normalized Cuts and Image Segmentation xp under can run 32-bit absolute
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Contour and Texture Analysis for Image Segmentation外籍学习资料-research reference： “Contour and Texture Analysis for Image Segmentation"
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Normalized Cut图像分割程序，matlab代码实现-Normalized Cut image segmentation procedure, Matlab code implementation
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Normalized cuts and image segmentation
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applies normalized cut on graph obtained from image and segment the image into specified number of regions.
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PCNN脉冲耦合神经网络，可应用与图像分割、边缘检测、细化、识别等方面。-PCNN pulse coupled neural network, and can be applied to image segmentation, edge detection, thinning and recognition, etc.
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Image Segmentation normalization method by J. Shi and J Malik, Normalized Cuts and Image Segmentation
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