×
验证码:
换一张
忘记密码?
记住我
CORC
首页
科研机构
检索
知识图谱
申请加入
托管服务
登录
注册
在结果中检索
科研机构
北京大学 [3]
北京航空航天大学 [1]
长春光学精密机械与物... [1]
武汉大学 [1]
内容类型
其他 [3]
期刊论文 [2]
会议论文 [1]
发表日期
2019 [1]
2015 [1]
2012 [1]
2009 [1]
2008 [1]
2006 [1]
更多...
×
知识图谱
CORC
开始提交
已提交作品
待认领作品
已认领作品
未提交全文
收藏管理
QQ客服
官方微博
反馈留言
浏览/检索结果:
共6条,第1-6条
帮助
已选(
0
)
清除
条数/页:
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
排序方式:
请选择
发表日期升序
发表日期降序
作者升序
作者降序
提交时间升序
提交时间降序
题名升序
题名降序
Spatial-temporal traffic outlier detection by coupling road level of service
期刊论文
IET INTELLIGENT TRANSPORT SYSTEMS, 2019, 卷号: 13, 页码: 1016-1022
作者:
Chen Yujun
;
Pu Juhua
;
Du Jiahong
;
Wang Yue
;
Xiong Zhang
收藏
  |  
浏览/下载:10/0
  |  
提交时间:2019/12/30
road traffic
data mining
traffic engineering computing
hidden Markov models
coupled hidden Markov model outlier detection method
Poisson mixture model
traffic congestion level
road flow
traffic flow
traffic outliers
data mining
city management
road level
spatial-temporal traffic outlier detection
upstream roads
road traffic congestion estimation
finer-grained outlier detection method
traffic anomalies
Distinguishing Specific and Daily Topics
其他
2015-01-01
Ge, Tao
;
Pei, Wenzhe
;
Chang, Baobao
;
Sui, Zhifang
收藏
  |  
浏览/下载:2/0
  |  
提交时间:2017/12/03
specific and daily topics
numeric features
Bayesian model
mixture of Poisson distribution
Bayesian Texture Classification Based on Contourlet Transform and BYY Harmony Learning of Poisson Mixtures
其他
2012-01-01
Dong, Yongsheng
;
Ma, Jinwen
收藏
  |  
浏览/下载:4/0
  |  
提交时间:2015/11/10
Bayesian Ying-Yang (BYY) harmony learning system
contourlet transform
model selection
Poisson mixtures
texture classification
AUTOMATED MODEL SELECTION
GAUSSIAN MIXTURE
IMAGE CLASSIFICATION
SCALE MIXTURES
EM ALGORITHM
SEGMENTATION
FEATURES
REPRESENTATION
DECOMPOSITION
EXTRACTION
Parameter estimation of Poisson mixture with automated model selection through BYY harmony learning
其他
2009-01-01
Ma, Jinwen
;
Liu, Jianfeng
;
Ren, Zhijie
收藏
  |  
浏览/下载:3/0
  |  
提交时间:2015/11/10
Bayesian Ying-Yang (BYY) harmony learning
Poisson mixture
Gradient learning algorithm
Automated model selection
Texture classification
GAUSSIAN MIXTURE
EM ALGORITHM
TEXTURE CLASSIFICATION
FINITE MIXTURE
DISTRIBUTIONS
NUMBER
COMPONENTS
Interval mapping for quantitative trait loci detection in finite mixture models with general kernel functions
期刊论文
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, 2008, 卷号: 37, 期号: 5
作者:
Wu, Xiaoxia
;
Chen, Hanfeng
;
Liu, Yanyan
收藏
  |  
浏览/下载:7/0
  |  
提交时间:2019/12/05
backcross
binomial mixture model
EM algorithm
identifiability
likelihood ratio test
normal mixture model
Poisson mixture model
CR image filter methods research based on wavelet-domain hidden markov models (EI CONFERENCE)
会议论文
ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China
Wang J.-L.
;
Wang Y.-P.
;
Li D.-Y.
;
Li S.-W.
;
Kui H.-L.
收藏
  |  
浏览/下载:17/0
  |  
提交时间:2013/03/25
In the procedure of computed radiography imaging
we should firstly get across the characters of kinds of noises and the relationship between the image signals and noises. Based on the specialties of computed radiography (CR) images and medical image processing
we have study the filtering methods for computed radiography images noises. On the base of analyzing computed radiography imaging system in detail
the author think that the major two noises are Gaussian white noise and Poisson noise. Then
the different relationship of between two kinds of noises and signal were studied completely. By considering both the characteristics of computed radiography images and the statistical features of wavelet transformed images
a multiscale image filtering algorithm
which based on two-state hidden markov model (HMM) and mixture Gaussian statistical model
has been used to decrease the Gaussian white noise in computed images. By using EM (Expectation Maximization) algorithm to estimate noise coefficients in each scale and obtain power spectrum matrix
then this carried through the syncretized two Filter that are IIR(infinite impulse response) Wiener Filter and HMM
according to scale size
and achieve the experiments as well as the comparison with other denoising methods were presented at last.
©版权所有 ©2017 CSpace - Powered by
CSpace