Description: 这个源码提供了如何利用高斯混合模型去做影像辨识的参数的训练-the source how to use a Gaussian mixture model to do the imaging parameters of literacy training Platform: |
Size: 22156 |
Author:小王 |
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Description: 一篇论文：基于EM算法的Gaussian混合模型用于图像分类-Paper: EM algorithm based on Gaussian mixture model for image classification Platform: |
Size: 263168 |
Author: |
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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. Platform: |
Size: 974848 |
Author:王磊 |
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Description: 一种基于肤色分割、区域分析和模板分布的彩色图像人脸检测算法.首先对输入的彩色图像利用混合高斯模型和亮度模型进行分割,然后根据人脸五官的结构特征对得到的区域进一步分析处理,获得所有可能的候选人脸.接着构造了一种基于双眼和人脸模板的概率模型并利用其对候选人脸进行最终检测.-Based on skin color segmentation, regional analysis and the template in color images of face detection algorithm. First, the input color image using mixture Gaussian model and the brightness model segmentation, then under the facial features of the structure on further analysis by region treatment to obtain all possible candidates face. then constructs a template based on the eyes and face the probability model and use its candidates face final test. Platform: |
Size: 586752 |
Author:麦高 |
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Description: 我自己写的关于二维混合高斯分布的EM算法-I wrote about the distribution of two-dimensional Gaussian mixture EM algorithm Platform: |
Size: 1024 |
Author:lwm |
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Description: 用于背景差分的高斯混合建模，实现运动目标检测，c++源码可编译执行-Difference of Gaussian mixture for the background model for moving object detection, c++ source code can be compiled implementation Platform: |
Size: 2755584 |
Author:chuck |
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Description: 星间精密测距是导航星座实现自主导航的核心技术。针对导航星座中码测量值精度低但无整周模糊度，载
波相位测量值精度高但存在整周模糊度的特点，该文根据贝叶斯递推原理提出了一种衰减记忆高斯和滤波(Fading
Memory Gaussian Sum Filter,FMGSF)的伪距估计方法。该方法用高斯和形式近似表示系统后验概率密度，并根
据卡尔曼滤波原理来更新高斯项的均值和方差，同时引入衰减记忆因子克服由于模型失配导致的滤波结果发散问
题，利用重采样解决由于载波相位测量值不确定导致的算法复杂度增加问题。理论分析和仿真结果表明，该文提出
的方法不仅能够克服周跳对伪距估计的影响，而且可以获得更好的测距精度。
-Precise ranging is the core technology of autonomous navigation.For code measurements yield noisy but
unambiguous pseudorange estimates and the pseudorange obtained with carrier phase measurements are almost
noiseless but are affected by integer ambiguity,a Fading Memory Gaussian Sum Filter(FMGSF)algorithm based
on Bayesian recursive relations is proposed.Posteriori probability density is approximated as a finite Gaussian
mixture,the means and variances of Gaussian terms are updated according to the principle of Kalman filter.
Fading memory factor is imported to avoid the issue of filter divergence due to mismodeling and resampling is
performed to resolve the issue of increasing in computational complexity caused by carrier phase measurement
uncertainty.Theoretical analysis and simulation results show that this algorithm can overcome the effect of cycle
slips to a certain extent and achieve higher range accuracy.
Platform: |
Size: 1083392 |
Author:wangmingxiao |
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Description: 这是一种新的自适应混合高斯算法，同基于传统混合高斯模型的运动目标检测
方法相比, 该方法有较好的自适应性, 能快速适应场景的变化-This is a new adaptive Gaussian mixture algorithm, compared with the traditional Gaussian mixture model-based moving target detection method, the method has better adaptability, able to adapt quickly to changes in the scene Platform: |
Size: 563200 |
Author:jim |
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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 Platform: |
Size: 1289216 |
Author:正东 |
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Description: 在VS2010中，利用openCV2.4.3写的基于混合高斯模型的运动目标检测。-moving object detection based on Gaussian mixture model in VS2010 and openCV2.4.3 .. Platform: |
Size: 4509696 |
Author:于洁 |
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Search - gaussian Mixture