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Description: 这个程序解决BMP图像的显示、反色、转灰度图、加高斯噪声、锐化、平滑等功能。注意：处理前要先将图像转成灰度图，否则不能处理。-this resolution BMP images showed that the anti-color, the gray level to increase Gaussian noise, sharpening, smoothing, and other functions. Caution : Prior to the conversion of image gray level they can handle.
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Size: 129405 |
Author: 陈慧 |
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Description: 这个程序解决BMP图像的显示、反色、转灰度图、加高斯噪声、锐化、平滑等功能。注意：处理前要先将图像转成灰度图，否则不能处理。-this resolution BMP images showed that the anti-color, the gray level to increase Gaussian noise, sharpening, smoothing, and other functions. Caution : Prior to the conversion of image gray level they can handle.
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Size: 2140160 |
Author: 陈慧 |
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Description: concerned with the blind identi cation of
bilinear systems excited by higher-order white noise. Un-
like prior work that restricted the bilinear system model to
simple forms and required the excitation to be Gaussian dis-
tributed, the results of this paper are applicable to a more
general class of bilinear systems and for the case when the
excitation is non-Gaussian. We describe an estimation pro-
cedure for the computation of the system parameters using
output cumulants of order less than four.
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Size: 1449984 |
Author: lzn |
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Description: 实现在参数先验是高斯分布情况下，利用马尔科夫链蒙特卡洛算法来对Logistic 回归模型参数的后验分布进行抽样
-It implements different Markov Chain Monte Carlo strategies for sampling from the posterior distribution over the parameter values for Logistic Regression models with a Gaussian prior on the parameter values.
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Size: 29696 |
Author: 王而山 |
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Description: 一篇关于代价参考粒子滤波算法的论文，该算法的优点是不需要任何先验概率知识的假定和重采样过程，可实现并行处理。本文将代价参考粒子滤波与当前统计模型的优点相结合 ,提出一种新的当前统计模型自适应跟踪算法 ,用于非线性非高斯系统的机动目标跟踪。-A particle filter on the reference price of the paper, the advantages of the algorithm does not require any prior knowledge of the assumptions and the probability of re-sampling process, enabling parallel processing. This will be the current price of reference particle filter and combine the advantages of statistical models, a new statistical model of the current adaptive tracking algorithm for nonlinear non-Gaussian target tracking systems.
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Size: 476160 |
Author: 刘帆 |
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Description: This package provides a Maximum Entropy Modeling toolkit written in C++ with Python binding. It includes:
Conditional Maximum Entropy Model
L-BFGS Parameter Estimation
GIS Parameter Estimation
Gaussian Prior Smoothing
C++ API
Python Extension module
Document and Tutorial -)-This package provides a Maximum Entropy Modeling toolkit written in C++ with Python binding. It includes:
Conditional Maximum Entropy Model
L-BFGS Parameter Estimation
GIS Parameter Estimation
Gaussian Prior Smoothing
C++ API
Python Extension module
Document and Tutorial -)
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Size: 764928 |
Author: shabo |
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Description: 《Software for Flexible Bayesian Modeling and Markov Chain Sampling》是机器学习领域专家Neal编写的用于Bayesian和马尔可夫链Linux下的C语言工具包。很有名，也很权威。
-This software supports Bayesian regression and classification models based on neural networks and Gaussian processes, and Bayesian density estimation and clustering using mixture models and Dirichlet diffusion trees. It also supports a variety of Markov chain sampling methods, which may be applied to distributions specified by simple formulas, including simple Bayesian models defined by formulas for the prior and likelihood.
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Size: 974848 |
Author: 王磊 |
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Description: 对三维图像进行求导的一个程序，利用高斯卷积后对图像求导相当于对高斯函数求导的性质。是用itk库写的一个通用模板类，里面还有例子。-Computation of local image derivatives is an important operation in many image processing tasks that
involve feature detection and extraction, such as edges, corners or more complicated features. How-
ever, derivative computation in discrete images is an ill-posed problem and derivative operators without
any prior smoothing are known to enhance noise. Here we present a new convolution operator, the
GaussianDerivativeOperator, that allows to calculate locally Gaussian derivatives of N order. Fur-
thermore, we present some useful classes and examples that make use of this new operator.
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Size: 866304 |
Author: xrx |
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Description: 在加入高斯白噪声的情况下,假设信噪比为10dB，形成传统波束形成法、Bartlett波束形成法、Capon波束形成法的空间谱图-Prior to joining the case of white Gaussian noise, assuming the SNR is 10dB, the formation of conventional beamforming method, Bartlett beamforming method, Capon Beamforming spectrum of space law
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Size: 1024 |
Author: vivi |
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Description: 本文提出了基于24位彩色图像对人脸进行识别的方法，介绍的主要内容是图像处理，它在整个软件中占有极其重要的地位，图像处理的好坏直接影响着定位和识别的准确率。本软件主要用到的图像处理技术是：光线补偿、高斯平滑和二值化。在识别前，先对图像进行补光处理，再通过肤色获得可能的脸部区域，最后根据人脸固有眼睛的对称性来确定是否就是人脸，同时采用高斯平滑来消除图像的噪声，再进行二值化，二值化主要采用局域取阈值方法，接下来就进行定位、提取特征值和识别等操作。经过测试，图像预处理模块对图像的处理达到了较好的效果，提高了定位和识别的正确率。-In this paper, a method based on 24-bit color images of the human face recognition, introduced the main contents of the image processing, it occupies a very important position in the entire software, image processing, direct impact on the positioning and recognition accuracy. This software is mainly used in image processing technology: light compensation, Gaussian smoothing and binarization. , Prior to the recognition, the first on the image processing of the fill light, then the face area obtained through color possible to determine whether that is the face, and finally, under the inherent symmetry of the eyes of the face, while using Gaussian smoothing to eliminate the noise of the image, and then binarization, binarization using local thresholding method, the next positioning operation of the extracted feature values and identification. Tested, image pre-processing module to image processing to achieve better results, locate and identify the correct rate.
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Size: 1428480 |
Author: nick |
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Description: CRFsuite: a fast implementation of Conditional Random Fields (CRFs)
CRFSuite is an implementation of Conditional Random Fields (CRFs) for labeling sequential data. The first priority of this software is to train and use CRF models as fast as possible even at the expense of its memory space and code generality. CRFsuite runs 5.4 - 61.8 times faster than C++ implementations for training. CRFsuite supports parameter estimation with L1 regularization (Laplacian prior) using Orthant-Wise Limited-memory Quasi-Newton (OW-LQN) method and L2 regularization (Gaussian prior) using Limited-memory BFGS (L-BFGS) method.,CRFsuite: a fast implementation of Conditional Random Fields (CRFs)
CRFSuite is an implementation of Conditional Random Fields (CRFs) for labeling sequential data. The first priority of this software is to train and use CRF models as fast as possible even at the expense of its memory space and code generality. CRFsuite runs 5.4- 61.8 times faster than C++ implementations for training. CRFsuite supports parameter estimation with L1 regularization (Laplacian prior) using Orthant-Wise Limited-memory Quasi-Newton (OW-LQN) method and L2 regularization (Gaussian prior) using Limited-memory BFGS (L-BFGS) method.
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Size: 29696 |
Author: icypriest |
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Description: CRFsuite: a fast implementation of Conditional Random Fields (CRFs)
CRFSuite is an implementation of Conditional Random Fields (CRFs) for labeling sequential data. The first priority of this software is to train and use CRF models as fast as possible even at the expense of its memory space and code generality. CRFsuite runs 5.4 - 61.8 times faster than C++ implementations for training. CRFsuite supports parameter estimation with L1 regularization (Laplacian prior) using Orthant-Wise Limited-memory Quasi-Newton (OW-LQN) method and L2 regularization (Gaussian prior) using Limited-memory BFGS (L-BFGS) method.,CRFsuite: a fast implementation of Conditional Random Fields (CRFs)
CRFSuite is an implementation of Conditional Random Fields (CRFs) for labeling sequential data. The first priority of this software is to train and use CRF models as fast as possible even at the expense of its memory space and code generality. CRFsuite runs 5.4- 61.8 times faster than C++ implementations for training. CRFsuite supports parameter estimation with L1 regularization (Laplacian prior) using Orthant-Wise Limited-memory Quasi-Newton (OW-LQN) method and L2 regularization (Gaussian prior) using Limited-memory BFGS (L-BFGS) method.
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Size: 1804288 |
Author: icypriest |
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Description: 本软件主要用到的图像处理技术是：光线补偿、高斯平滑和二值化。在识别前，先对图像进行补光处理，再通过肤色获得可能的脸部区域，最后根据人脸固有眼睛的对称性来确定是否就是人脸，同时采用高斯平滑来消除图像的噪声，再进行二值化，二值化主要采用局域取阈值方法，接下来就进行定位、提取特征值和识别等操作。经过测试，图像预处理模块对图像的处理达到了较好的效果，提高了定位和识别的正确率。-This software is mainly used in image processing technology: light compensation, Gaussian smoothing and binarization. , Prior to the recognition, the first on the image processing of the fill light, then the face area obtained through color possible to determine whether that is the face, and finally, under the inherent symmetry of the eyes of the face, while using Gaussian smoothing to eliminate the noise of the image, and then binarization, binarization using local thresholding method, the next positioning operation of the extracted feature values and identification. Tested, image pre-processing module to image processing to achieve better results, locate and identify the correct rate.
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Size: 1312768 |
Author: 牛满 |
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Description: 针对概率假设密度 (Probability hypothesis density, PHD) 高斯混合实现算法中的分量删减问题, 提出了基于
Dirichlet 分布的分量删减算法以改进概率假设密度高斯混合实现算法的性能. 算法采用极大后验准则估计混合参数, 采用仅
依赖于混合权重的负指数 Dirichlet 分布作为混合参数的先验分布, 利用拉格朗日乘子推导了混合权重的更新公式. 算法利用
负指数 Dirichlet 分布的不稳定性, 在极大后验迭代过程中驱使与目标强度不相关的分量消亡. 该不稳定性还能够解决多个相
近分量共同描述一个强度峰值的问题-As far as component pruning in Gaussian mixture (GM) implementation of probability hypothesis density
(PHD) is concerned, a component pruning algorithm based on Dirichlet distribution is proposed to improve the performance
of Gaussian mixture implementation of probability hypothesis density. The maximum a posterior criterion is adopted for
estimation of mixing parameters. Dirichlet distribution with negative exponent parameters, which only depends on mixing
weights, is adopted as the prior distribution of mixing parameters. The update formulation of mixing weight is derived by
Lagrange multiplier. The instability of Dirichlet distribution with negative exponent parameters is applied to driving the
components irrelevant with target intensity to extinction during the maximum a posterior iteration. Besides, the problem
that one peak of intensity is presented by several proximate mixing component
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Size: 1289216 |
Author: 正东 |
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Description: 应用于超分辨成像； 图像处理； 图像重建；图像恢复(Deconvolution using natural image priors Gaussian Prior Deconvolution)
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Size: 718848 |
Author: luvky_girl |
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Description: 高斯过程回归是一种基于贝叶斯原理的统计机器学习方法，将先验分布通过贝叶斯定理转化成后验分布，与其他没有采用贝叶斯技巧的预测方法而言，高斯过程最大的优点是能方便地推断出超参数，同时也能方便地给出预测值的置信区间(Gaussian Process Regression is a statistical machine learning method based on Bayesian principle. It transforms prior distribution into posterior distribution by Bayesian theorem. The greatest advantage of Gauss process regression is that it can easily infer Super-parameters and give confidence interval of predicted values.)
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Size: 4909056 |
Author: 杨哽哽 |
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