Detecting parametric objects in large scenes by Monte Carlo sampling

DOI10.1007/s11263-013-0641-0zbMath1328.68282OpenAlexW2064610368MaRDI QIDQ903428

Yannick Verdié, Florent Lafarge

Publication date: 6 January 2016

Published in: International Journal of Computer Vision (Search for Journal in Brave)

Full work available at URL: https://hal.inria.fr/hal-00843022/file/IJCV.pdf

zbMATH Keywords

energy minimizationstochastic modelingpoint processesMonte Carlo samplingMarkov random fieldsobject detectionlarge scenes

Mathematics Subject Classification ID

Random fields; image analysis (62M40) Computing methodologies for image processing (68U10) Machine vision and scene understanding (68T45)

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Cites Work

• Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
• Jump-diffusion Markov processes on orthogonal groups for object pose estimation
• On the optimality of solutions of the max-product belief-propagation algorithm in arbitrary graphs
• Facts, Conjectures, and Improvements for Simulated Annealing
• A Marked Point Process for Modeling Lidar Waveforms
• Monte Carlo sampling methods using Markov chains and their applications
• Monte Carlo strategies in scientific computing
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