Description: 采用期望最大算法最优化初始高斯混合模型的程序，笔者借用ubm自适应方法，很好的解决了模型不收敛的问题。-expectations largest algorithm using optimization initial Gaussian mixture model procedures, the author borrowed ubm adaptive method, a good model is not the solution convergence problem. Platform: |
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Author:gqy |
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Description: 采用期望最大算法最优化初始高斯混合模型的程序，笔者借用ubm自适应方法，很好的解决了模型不收敛的问题。-expectations largest algorithm using optimization initial Gaussian mixture model procedures, the author borrowed ubm adaptive method, a good model is not the solution convergence problem. Platform: |
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Description: 学位论文；运动物体跟踪方法主要包括卡尔曼滤波，Mean-shift，Camshifi算法，粒子滤波器，Snake模型等；应用卡尔曼滤波方法设计了一套煤矿矿工出入自动监测系统；提出了一种新的基于高斯混合模型的颜色特征提取方法，该方法克服了现有的Camshift算法Continuousl y Adaptive eanshift中跟踪目标特征提取精确度低和计算复杂度高的缺陷-Dissertation moving object tracking methods include Kalman filtering, Mean-shift, Camshifi algorithm, particle filter, Snake model the application of Kalman filtering method designed a coal miners out of automatic monitoring system a new Gaussian mixture model based on the color feature extraction method to overcome the existing Camshift algorithm Continuousl y Adaptive eanshift track target feature extraction accuracy and low computational complexity and high defects Platform: |
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Author:田卉 |
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Description: This can be useful to Understand the Gaussian Mixture Model for Adaptive Background updation Platform: |
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Author:Rajaram |
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Description: This can be useful to Understand the Gaussian Mixture Model for Adaptive Background updation Platform: |
Size: 182272 |
Author:Rajaram |
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Description: This can be useful to Understand the Gaussian Mixture Model for Adaptive Background updation Platform: |
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Author:Rajaram |
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Description: A common method for real-time segmentation of
moving regions in image sequences involves “background
subtraction,” or thresholding the error between
an estimate of the image without moving objects and
the current image. The numerous approaches to this
problem differ in the type of background model used
and the procedure used to update the model. This paper
discusses modeling each pixel as a mixture of Gaussians
and using an on-line approximation to update
the model. The Gaussian distributions of the adaptive
mixture model are then evaluated to determine which
are most likelyt o result from a background process.
Each pixel is classified based on whether the Gaussian
distribution which represents it most effectivelyis considered
part of the background model. Platform: |
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Author:ajinkya |
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Description: 关于自适应高斯混合模型球场检测算法及其在体育视频分析中的应用-Adaptive Gaussian mixture model of pitch detection algorithm and its application in sports video analysis Platform: |
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Author:张文 |
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Description: !本文提出了一种静止摄像机条件下的运动目标检测与跟踪算法,它以一种改进的自适应混合高
斯模型为背景更新方法-用连通区检测算法分割出前景目标-以./01/2滤波为运动模型实现对运动目标的
连续跟踪-! This paper, a stationary camera for moving target detection and tracking algorithm, based on a modified adaptive Gaussian mixture model for the background updating- with Components of the segment the foreground object detection algorithm- to ./01/2 filter for the motion model to achieve continuous tracking of the moving target Platform: |
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Author:xxx |
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Description: 自适应的选择高斯混合模型个数，并用EM算法估计参数-Adaptive selection of the number of Gaussian mixture model and estimated parameters using EM algorithm Platform: |
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Author:ccj |
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Description: 高斯混合模型，从97年到2011年间经典英文文献13篇，对与学习高斯模型的同学很有帮助-Gaussian mixture model, the classic English literature from 1997 to 2011, 13, very helpful with the students to learn the Gaussian model Platform: |
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Author:国际米兰 |
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Description: 混合高斯背景建模C++源程序
在进行前景检测前，先对背景进行训练，对图像中每个背景采用一个混合高斯模型进行模拟，每个背景的混合高斯的个数可以自适应。-Before the the mixed Gaussian background the modeling C++ source. During foreground detection, background training, each background image using a Gaussian mixture model simulation, adaptive Gaussian mixture number of each background. Platform: |
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Author:sjtu-lsz |
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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: 针对智能交通系统中运动目标检测阶段存在的不足，提出了一种基于自适应混合高斯模型（ＧＭＭ）的改进算法。-For the deficiencies of the intelligent transportation system moving target detection stage, an improved algorithm based on adaptive Gaussian mixture model (GMM). Platform: |
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Author:张子龙 |
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Description: 提出了一种基于模型切换的背景建模方法(M SBM ).该方法以嫡图像为纽带, 实现了不同精细程度的背景模型在空间上的自适应选取和在时间上的自适应切换.对于亮度分布复杂度高的背景区域采用精细的模型以保证运动目标检测的精度,反之采用简单的模型以降低计算量
.通过模型结构自适应结合参数自适应, 很好地兼顾了检测精度和计算代价.墓于高斯混合模型和时间平均模型的双模型切换式运动目标检测算法被用于实验研究, 结果表明这种算法的检测效果和单独采用高斯混合模型的检测效果相当, 而计算速度却比后者提高很多-Proposed a model of background modeling method switching (M SBM) Based on the method of entropy image as a link, to achieve different degrees of background model in the fine spatial adaptive selection and adaptive switching in time. For high brightness distribution complex background area using sophisticated models to ensure the accuracy of the moving target detection, whereas a simple model to reduce the amount of computation
By combining adaptive parameter adaptive model structure, a good balance between detection accuracy and computational cost dual model tomb on Gaussian mixture model and the time-averaged model switched moving target detection algorithm is used for experimental research results show that the detection algorithm using Gaussian mixture model and a separate testing results have been very, and improve computing speed than many of the latter Platform: |
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Search - adaptive gaussian mixture model