Paper Info
Title
Estimation of the weight parameter with SAEM for marked point processes applied to object detection
Abstract
We consider the problem of estimating one of the parameters of a marked point process, namely the tradeoff parameter between the data and prior energy terms defining the probability density of the process. In previous work, the Stochastic Expectation-Maximization (SEM) algorithm was used. However, SEM is well known for having bad convergence properties, which might also slow down the estimation time. Therefore, in this work, we consider an alternative to SEM: the Stochastic Approximation EM algorithm, which makes an efficient use of all the data simulated. We compare both approaches on high resolution satellite images where the objective is to detect boats in a harbor.
Year
Venue
Keywords
2014
Signal Processing Conference
image resolution,object detection,stochastic processes,SAEM,SEM algorithm,boat detection,high resolution satellite images,marked point process,object detection,probability density,stochastic approximation EM algorithm,stochastic expectation-maximization algorithm,weight parameter estimation,Image processing,Stochastic Approximation EM,Stochastic EM,marked point process,object detection
Field
DocType
ISSN
Convergence (routing),Object detection,Satellite,Mathematical optimization,Expectation–maximization algorithm,Point process,Algorithm,Image processing,Probability density function,Stochastic approximation,Mathematics
Conference
2076-1465
Citations 
PageRank 
References 
0
0.34
1
Authors
2
Name
Order
Citations
PageRank
Aurelie Boisbunon100.34
Josiane Zerubia22032232.91