2022年8月1日 · The patch group based Gaussian Mixture Model (PG-GMM) learning algorithm is used to learn the nonlocal self-similarity (NSS) prior from training images. Then the phase information is modeled by learned patch group priors, and further optimized using the steps of gaussian component selection and weighted sparse coding.

External Prior Guided Internal Prior Learning for Real-World Noisy Image Denoising. IEEE Transactions on Image Processing (TIP), 2018. Please cite the paper if you are using this code in your research. Please see the file License.txt for the license governing this code. Version: 1.0 (03/28/2018), see ChangeLog.txt.

2022年8月1日 · This paper proposes a novel non-contact target-free video motion estimation method based on GMM by utilizing the image priors on phase patches. PGs are extracted from training images by putting nonlocal similar patches into groups, and the GMM learning algorithm is used to learn the NSS prior.

任务参数化高斯混合模型 (TP-GMM)旨在利用任务参数的功能性特性来提高泛化能力。 事实上，在机器人应用中，任务参数在大多数情况下都与参考系、坐标系、基本 函数 或局部投影有关，这些结构可以用来加速学习，并为系统提供更好的泛化能力。 图1，右图为四个演示，褐紫色框为出发点，到达不同的黄色框目标点，是两个不同上午观测坐标系。 标准的回归方法是以黄色框的位姿向量为输入，动作模型参数即高斯混合模型参数向量为输出进行训练，但由此得到的模型泛化性 …

什么是PGMM模型（Penalized Gaussian Mixed Model）?如何正确应用？ 计量经济学中面板数据，好不容易摸爬打滚看完GMM（高斯混合模型），现在要继续啃PGMM，晕掉了，求大神解…

高斯混合模型是一种无监督 聚类算法，一般使用EM 算法 进行求解。 Kmeans VS GMM：Kmeans算法本质上用的也是EM算法求解的。 定义：高斯混合模型是指具有如下形式的概率分布模型： p ( y ∣ θ ) = ∑ k = 1 K α k ϕ ( y ∣ θ k ) (1) p (y|\theta) = \sum_ {k=1}^K \alpha_k \phi (y|\theta_k) \tag1 p(y∣θ) = k=1∑K αkϕ(y∣θk) (1)

2022年8月1日 · The patch group based Gaussian Mixture Model (PG-GMM) learning algorithm is used to learn the nonlocal self-similarity (NSS) prior from training...

2019年6月27日 · 高斯混合模型（Gaussian Mixture Model，简称GMM）是一种常用的概率模型，用于对复杂的数据分布进行建模和拟合。GMM的目标是根据这些数据来估计模型的参数，包括每个分量的均值、协方差和混合系数（表示每个分量在整个数据集中的权重）。

This package contains the inference implementation (Gibbs Sampler) for the "Physically Consistent Bayesian Non-Parametric Mixture Model" (PC-GMM) proposed in [1].

2016年2月18日 · In this paper, we propose a patch group (PG) based NSS prior learning scheme to learn explicit NSS models from natural images for high performance denoising. PGs are extracted from training images by putting nonlocal similar patches into groups, and a PG based Gaussian Mixture Model (PG-GMM) learning algorithm is developed to learn the NSS prior.