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Description: 马尔科夫随机场在图像分析中的应用，需要了解马尔科夫算法的同学将是最好的资料
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Size: 5807861 |
Author: liuliuzu |
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Description: 马尔科夫随机场(MRF)基准，包括MRF库和验证图像-code for all the benchmarks, including the MRF library and imageLib
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Size: 14309376 |
Author: 路云 |
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Description: 马尔科夫随机场，用于计算机视觉，图像分割，目标检测等。-Markov random field, used in computer vision, image segmentation, target detection.
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Size: 58368 |
Author: 栋栋 |
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Description: 马尔科夫随机场matlab代码 希望有所帮组-Matlab MRF code hope it will be helpful to you
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Size: 31744 |
Author: chenfeng |
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Description: MRF程序：建立了马尔科夫随机场 应用于图像去噪 不过此程序有待进一步调试-MRF procedure used in image denoising
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Size: 18432 |
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Description: 高斯马尔科夫随机场和模糊c均值聚类的matlab源程序-Gassian Markov random filed and fuzzy c means clustering matlab source code
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Size: 32768 |
Author: Y.Meng |
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Description: hmm, mem, crf简介
hmm：hidden markov model 隐马尔科夫模型
mem: maximum entropy model 最大熵模型
crf: conditional random field 条件随机场模型-hmm, mem, crf Profile hmm: hidden markov model mem: maximum entropy model model crf: conditional random field
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Size: 827392 |
Author: ly |
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Description: 一种基于马尔科夫随机场的图像分割matlab源码，包含ICM迭代条件模式求解最大后验概率算法，已通过测试。-Markov random field based image segmentation matlab source code, including the ICM iteration conditions for solving the maximum a posteriori probability model algorithm has been tested.
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Size: 19456 |
Author: 包裹 |
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Description: 在学习马尔科夫随机场的过程中用到的一些文献，有助于理解马尔科夫随机场-The literatures used in the learning of Markov random field.It can help to understand the Markov random field.
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Size: 21885952 |
Author: tyksango |
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Description: 介绍了图像分割中使用马尔科夫随机场的综述，很好用的一篇文章-Describes the image segmentation using Markov random field in the review, very good article
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Size: 258048 |
Author: tim |
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Description: 隐马尔科夫随机场的demo，有助于初学者了解隐马尔科夫随机场-HMM demo
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Size: 104448 |
Author: 玉玉 |
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Description: 一个很好的马尔科夫随机场（MRF）模型应用实例。-Examples for the application of markov random field.
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Size: 21504 |
Author: Andy |
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Description: 马尔科夫随机场，用于进行图像处理，如背景抑制等-Markov random field for image processing, such as background suppression
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Size: 493568 |
Author: sanger |
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Description: 基于马尔科夫随机场的，例子学习超分辨率复原代码。-This is an implementation of the example-based super-resolution algorithm. Although the applications of MSFs have now extended beyond example-based super resolution and texture synthesis, it is still of great value to revisit this problem, especially to share the source code and examplar images with the research community. We hope that this software package can help to understand Markov random fields for low-level vision, and to create benchmark for super-resolution algorithms.
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Size: 37349376 |
Author: 任伟彦 |
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Description: 基于马尔科夫随机场的SAR图像分割方法，对应于采用最大后验概率准则对SAR目标切片图像分割，采用聚类分析算法求解。-SAR image segmentation based on Markov random fields, which corresponds to using the maximum posterior probability criteria for SAR Target Chip Image segmentation using cluster analysis algorithm for
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Size: 19456 |
Author: 赵燕 |
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Description: 计算机视觉大牛Andrew Blake著的书。
《视觉和图像处理中的马尔科夫随机场》
Markov random fields for vision and image processing-This book sets out to demonstrate the power of the Markov random field (MRF) in vision.It treats the MRF both as a tool for modeling image data and, coupled with a set of recently
developed algorithms, as a means of making inferences about images. The inferences concern underlying image and scene structure to solve problems such as image reconstruction,
image segmentation, 3D vision, and object labeling. This chapter is designed to present some of the main concepts used in MRFs, both as a taster and as a gateway to the more
detailed chapters that follow, as well as a stand-alone introduction to MRFs.
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Size: 5147648 |
Author: xuyuhua |
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Description: 基于EM-马尔科夫随机场-ICM的图像分割程序，很好的学习代码-EM-based Markov random-ICM image segmentation procedures, good learning code
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Size: 9678848 |
Author: tyj |
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Description: 关于马尔科夫随机场的分割matlab代码(On the Markov random field segmentation matlab code)
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Size: 21504 |
Author: Xs_55555
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Description: 基于马尔科夫随机场算法(MRF)的图像分割(Image segmentation of Markov random field algorithm)
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Size: 3072 |
Author: niuxiaoniu
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Description: 马尔科夫随机场一般是离散的。当然也有连续的马尔可夫随机场，如果假设这个马尔可夫随机场定义在一个域U上面而且任意x∈U，Y(x)都是服从高斯分布的，而且两点间的covariance function K(x,y)=G(x,y)，where G是对应U的格林函数，那么这个随机场既是马尔可夫随机场，也是高斯随机场。(The Markov random field is generally discrete. Of course there are continuous Markov random field, assuming that the Markov random field defined in a domain of U and above any X in U, Y (x) are subject to Gauss distribution, and between the two covariance function K (x, y) =G (x, y), where G is the corresponding Green function U, then this is a random field Markov random field, but also with the Gauss airport.)
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Size: 312320 |
Author: sixiudant |
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