文件名称:gaussian
- 所属分类：
- 图形图像处理(光照,映射..)
- 资源属性：
- [PDF]
- 上传时间：
- 2012-11-26
- 文件大小：
- 7.45mb
- 下载次数：
- 0次
- 提 供 者：
- 国***
- 相关连接：
- 无
- 下载说明：
- 别用迅雷下载，失败请重下，重下不扣分！
介绍说明－－下载内容均来自于网络，请自行研究使用
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
(系统自动生成,下载前可以参看下载内容)
下载文件列表
1997-Pfinder Real-time tracking of the human body(引用349).pdf
1999-stauffer_Adaptive background mixture models for real-time tracking（引用83）.pdf
2000-Learning Patterns of Activity Using Real-Time Tracking(引用480).pdf
2001-An Improved adaptive background mixture model for real-time tracking with shadow detection.pdf
2001-Multi-Object Tracking Using Dynamical Graph Matching.pdf
2002-Background and foreground modeling using nonparametric kernel density estimation for visual surveillance(引用201).pdf
2002-Understanding Background Mixture Models for Foreground Segmentation.pdf
2004-Improved adaptive Gaussian mixture model for background subtraction（引用28）.pdf
2004-Recursive Unsupervised Learning of Finite Mixture Models(引用32).pdf
2005-Effective gaussian mixture learning for video Background Subtraction（引用95）.pdf
2006-Efficient adaptive density estimation per image pixel for the task of background subtraction.pdf
2010-On the analysis of background subtraction techniques using Gaussian mixture models.pdf
2011-Regularized background Adaptation a novel learning rate control scheme for Gaussian mixture modeling.pdf
1999-stauffer_Adaptive background mixture models for real-time tracking（引用83）.pdf
2000-Learning Patterns of Activity Using Real-Time Tracking(引用480).pdf
2001-An Improved adaptive background mixture model for real-time tracking with shadow detection.pdf
2001-Multi-Object Tracking Using Dynamical Graph Matching.pdf
2002-Background and foreground modeling using nonparametric kernel density estimation for visual surveillance(引用201).pdf
2002-Understanding Background Mixture Models for Foreground Segmentation.pdf
2004-Improved adaptive Gaussian mixture model for background subtraction（引用28）.pdf
2004-Recursive Unsupervised Learning of Finite Mixture Models(引用32).pdf
2005-Effective gaussian mixture learning for video Background Subtraction（引用95）.pdf
2006-Efficient adaptive density estimation per image pixel for the task of background subtraction.pdf
2010-On the analysis of background subtraction techniques using Gaussian mixture models.pdf
2011-Regularized background Adaptation a novel learning rate control scheme for Gaussian mixture modeling.pdf