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Editors: Sequential Monte Carlo Methods in Practice
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The article was published on 2001-01-01 and is currently open access. It has received 1215 citations till now. The article focuses on the topics: Dynamic Monte Carlo method & Monte Carlo method in statistical physics.read more
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Book ChapterDOI
Extracting Moving People from Internet Videos
TL;DR: This work proposes a fully automatic framework to detect and extract arbitrary human motion volumes from real-world videos collected from YouTube and applies a top-down pictorial structure model to complete the extraction of the humans in arbitrary motion.
Book ChapterDOI
Shape Particle Filtering for Image Segmentation
Marleen de Bruijne,Mads Nielsen +1 more
TL;DR: Current methods elegantly incorporate global shape and appearance, but can not cope with localized appearance variations and rely on an assumption of Gaussian gray value distribution, so initialization near the optimal solution is required.
Journal ArticleDOI
Analysing time-varying trends in stratospheric ozone time series using the state space approach
TL;DR: In this article, a hierarchical statistical state space model for ozone profile time series is presented. The time series are from satellite measurements by the Stratospheric Aerosol and Gas Experiment (SAGE) II and the Global Ozone Monitoring by Occultation of Stars (GOMOS) instruments spanning the years 1984-2011.
Journal ArticleDOI
Performance comparison of EKF and particle filtering methods for maneuvering targets
TL;DR: This paper focuses on the application of the recently proposed cost-reference particle filtering (CRPF) methodology, which aims at the estimation of the system state without using probability distributions.
Proceedings ArticleDOI
Joint multiple target tracking and classification in collaborative sensor networks
TL;DR: An algorithm based on sequential Monte Carlo (SMC) filtering of jump Markov systems to jointly track the system dynamic and classify the targets and an optimal sensor selection scheme based on the maximization of the expected mutual information is integrated naturally within the SMC tracking framework.