Description: ：介绍了扩展卡尔曼滤波算法和无迹变换(unscented transformation，UT)算法，并对扩展卡尔曼滤波算法(EKF)和无
迹卡尔曼滤波算法(UKF)进行比较，阐明了UKF优于EKF。在此基础上，提出了一种基于Unscented变换(uT)的高斯和滤
波算法，该算法首先通过合并准则得到适当个数的混合高斯模型，逼近系统中非高斯噪声的概率密度-: Introduction of the extended Kalman filter algorithm and unscented transform (unscented transformation, UT) algorithm, the extended Kalman filter algorithm (EKF) and unscented Kalman filter (UKF) for comparison to clarify the UKF is superior to EKF. On this basis, we propose an approach based on Unscented Transform (uT) and the Gaussian filtering algorithm, which first of all, by merging the appropriate number of criteria to be a mixture of Gaussian model, which was close to the system of the Central African Gaussian noise probability density Platform: |
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Author:lyh |
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Description: 1.选择3种边缘检测算子对一幅图像进行边缘检测，显示检测结果，对检测结果进行比较分析。
2.对混有高斯噪声的图像进行边缘处理，比较各边缘检测算子对噪声的敏感性。
-1. Choice of three kinds of edge detection operator of an image edge detection, test results show that, on a comparative analysis of test results. 2. For a mixture of Gaussian noise the edge of the image processing, edge detection for each operator to noise sensitivity. Platform: |
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Author:syq |
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Description: 双高斯混合分布和高斯拉普拉斯混合分布的噪声仿真程序，用于分析闪烁噪声-Double Gaussian mixture Laplacian distribution and Gaussian mixture distribution noise simulation program for the analysis of flicker noise Platform: |
Size: 1024 |
Author:姚超军 |
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Description: 这个程序用于在图片中增加各种噪声，如高斯椒盐噪声, 加性或乘性等多种混合噪声，用于其它程序的测试。-This procedure is used to increase the variety of picture noise, such as salt and pepper Gaussian noise, additive or multiplicative noise, such as a mixture for testing other programs. Platform: |
Size: 8192 |
Author:xlz |
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Description: :高斯混合模型(GMM)是一种经典的说话人识别算法，本文在实现其算法的同时，主要模拟了不同噪声环境情况下高斯混合模型
(GMM)的杭嗓声性能，得到了一些有益结论。
-Gaussian mixture model (GMM) is a classic speaker recognition algorithms, this algorithm at the same time in fulfilling its main simulated environmental conditions under different noise Gaussian mixture model
(GMM) of the Hang throat sound performance, and obtained some useful conclusions.
Platform: |
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Author:于高 |
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Description: 给一段视频加噪声，并通过混合高斯模型提取背景，可以验证混合高斯模型对噪声的鲁棒性！有注释！-To add a video noise, and Gaussian mixture model by extracting the background, you can verify the Gaussian mixture model robustness to noise! A comment! Platform: |
Size: 3072 |
Author:yaoqiuxiang |
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Description: 用平均背景法去除背景，算法思想比混合高斯模型和codebook模型简单，适合提取背景变化较小的场景。用中值滤波去除掉产生的椒盐噪声-Average method to remove the background with the background, the algorithm thought codebook than the Gaussian mixture model and a simple model for extraction of small changes in background scene. With a median filter to remove salt and pepper noise generated Platform: |
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Author: |
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Description: 基于Directionlet变换的图像去噪与增强的算法研究；基于提升Directionlet域高斯混合尺度模型的图像噪声抑制等。-Image denoising based Directionlet transform algorithm and enhanced Gaussian mixture based on lifting Directionlet field-scale model of image noise suppression. Platform: |
Size: 13386752 |
Author:zhangguodong |
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Description: Traditional single particle reconstruction methods use either the Fourier or the
delta function basis to represent the particle density map. We propose a more
flexible algorithm that adaptively chooses the basis based on the data. Because
the basis adapts to the data, the reconstruction resolution and signal-to-noise
ratio (SNR) is improved compared to a reconstruction with a fixed basis.
-This is a 3D visualization of how the Expectation Maximization algorithm learns a Gaussian Mixture Model for 3-dimensional data.
Platform: |
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Author:liumang |
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Description: 不错的GM_EM代码。用于聚类分析等方面。- GM_EM- fit a Gaussian mixture model to N points located in n-dimensional
space.
Note: This function requires the Statistical Toolbox and, if you wish to
plot (for k = 2), the function error_ellipse
Elementary usage:
GM_EM(X,k)- fit a GMM to X, where X is N x n and k is the number of
clusters. Algorithm follows steps outlined in Bishop
(2009) Pattern Recognition and Machine Learning , Chapter 9.
Additional inputs:
bn_noise- allow for uniform background noise term ( T or F ,
default T ). If T , relevant classification uses the
(k+1)th cluster
reps- number of repetitions with different initial conditions
(default = 10). Note: only the best fit (in a likelihood sense) is
returned.
max_iters- maximum iteration number for EM algorithm (default = 100)
tol- tolerance value (default = 0.01)
Outputs
idx- classification/labelling of data in X
mu- GM centres Platform: |
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Author:朱魏 |
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Description: 研究表明超高斯分布更加贴近语音信号的实际分布，然而语音信号很难用单一的概率密度
函数准确描述，针对这一情况，提出了一种用超高斯混合模型对语音信号幅度谱建模的新方法，并推导了
基于此模型的幅度谱最小均方误差估的估计式。仿真结果表明：与传统的短时谱估计算法相比，该算法不
仅能够进一步提高增强语音的信噪比，而且可以有效减小增强语音的失真度，提高增强语音的主观感知
质量。 -Recent research indicates that the speech spectral amplitude distributions could
be fairly described with super-Gaussian probability density function. However, the complexity
of speech signal determines that the distribution statistics ofspeech signal could not be well
described by single simple function. Thus a super-Gaussian mixture model for speech spectral
amplitude is proposed, and with this model, a minimum mean-square error (MMSE) estimator for speech
signals spectral amplitude is derived. The simulation results show that this algorithm based on
Gaussian and super-Gaussian speech model could achieve better noise suppression and lower speech
distortion as compared with the conventional short-time spectral amplitude estimation algorithm. Platform: |
Size: 957440 |
Author:立枣酒 |
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Description: 传统的短时谱估计语音增强算法通常假设语音谱分量相互独立，没有考虑语音谱分量间的相关性。针对这
一问题，该文提出一种新的基于多元Laplace分布模型的短时谱估计算法。首先，假设语音的离散余弦变换(DCT)
系数服从多元Laplace分布，以此利用谱分量间的相关性；在此基础上，利用多元随机矢量的高斯尺度混合模型表
示，推导得到语音DCT系数矢量的最小均方误差(MMSE)估计的解析表达式；并进一步推导了基于该分布模型的
语音存在概率，对最小均方误差估计子进行修正。实验结果表明，该算法在抑制背景噪声和减少语音失真等方面优
于传统的语音增强方法。-The spectral components of speech are usually assumed to be independent in traditional short-time
spectrum estimation, which is not the case in practice. Tosolve this problem, a new speech enhancement algorithm
with multivariate Laplace speech model is proposed in this paper. Firstly, the speech Discrete Cosine Transform
(DCT) coefficients are modeled by a multivariate Laplace distribution, so the correlations between speech spectral
components can be exploited. And then a Minimum-Mean-Square-Error (MMSE) estimator based on the proposed
model is derived using a Gaussian scale mixture representation of random vectors. Furthermore, the speech
presence uncertainty with the new model is derived to modify the MMSE estimator. Experimental results show
that the developed method has better noise suppression performance and lower speech distortion compared to the
traditional speech enhancement method. Platform: |
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Author:立枣酒 |
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Description:
BLS-GSM代表“Bayesian Least Squares - Gaussian Scale Mixture(贝叶斯最小二乘-高斯尺度混合模型)”。
这个工具箱实现了该篇论文中介绍的算法：
J Portilla, V Strela, M Wainwright, E P Simoncelli, Image Denoising
using Scale Mixtures of Gaussians in the Wavelet Domain, IEEE
Transactions on Image Processing. vol 12, no. 11, pp. 1338-1351,
November 2003
这个工具箱进行图像去噪时，假定噪声类型为高斯噪声并且我们知道它的功率谱密度（不需要是白色的）。-BLS-GSM stands for Bayesian Least Squares- Gaussian Scale Mixture .
This toolbox implements the algorithm described in:
J Portilla, V Strela, M Wainwright, E P Simoncelli, Image Denoising
using Scale Mixtures of Gaussians in the Wavelet Domain, IEEE
Transactions on Image Processing. vol 12, no. 11, pp. 1338-1351,
November 2003
This
tool implements image denoising assuming that the noise is Gaussian,
and that we know its power spectral density (it does not need to be white). Platform: |
Size: 999424 |
Author:沙天飞 |
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Description: 在高斯混合噪声背景下实现SLIM谱估计算法和l1范数SLIM谱估计法，并在-5到15的信噪比条件与CRLB对比均方频率误差-the paper achieves the SLIM spectral estimation algorithm and l1-SLIM spectral estimation method under the Gaussian mixture background noise,then compares the mean square frequency error with CRLB under SNR from-5 to 15 Platform: |
Size: 5120 |
Author:xiaohui |
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Description: 基于帧间差分的单目标/多目标的实时跟踪程序，基于MATLAB编写。希望对刚学习MATLAB的同学有所帮助-This example shows how to perform automatic detection and motion-based
tracking of moving objects in a video a stationary camera.
Copyright 2014 The MathWorks, Inc.
Detection of moving objects and motion-based tracking are important
components of many computer vision applications, including activity
recognition, traffic monitoring, and automotive safety. The problem of
motion-based object tracking can be divided into two parts:
# detecting moving objects in each frame
# associating the detections corresponding to the same object over time
The detection of moving objects uses a background subtraction algorithm
based on Gaussian mixture models. Morphological operations are applied to
the resulting foreground mask to eliminate noise. Finally, blob analysis
detects groups of connected pixels, which are likely to correspond to
moving objects.
The association of detections to the same object is based solely on
motion. The mo Platform: |
Size: 1024 |
Author:窝 |
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Search - gaussian mixture noise