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Conference papers
Learning viewpoint planning in active recognition on a small sampling budget: a Kriging approach
Abstract : This paper focuses on viewpoint planning for 3D active object recognition. The objective is to design a planning policy into a Q-learning framework with a limited number of samples. Most existing stochastic techniques are therefore inapplicable. We propose to use Kriging and Bayesian Optimization coupled with Q-learning to obtain a computationally-efficient viewpoint-planning design, under a restrictive sampling budget. Experimental results on a representative database, including a comparison with classical approaches, show promising results for this strategy.
Cited literature [15 references]
https://hal-supelec.archives-ouvertes.fr/hal-00520814
Contributor : Julien Marzat <>
Submitted on : Thursday, September 8, 2011 - 10:09:35 AM
Last modification on : Wednesday, September 16, 2020 - 4:42:38 PM
Long-term archiving on: : Tuesday, November 13, 2012 - 10:06:19 AM
Contributor : Julien Marzat <>
Submitted on : Thursday, September 8, 2011 - 10:09:35 AM
Last modification on : Wednesday, September 16, 2020 - 4:42:38 PM
Long-term archiving on: : Tuesday, November 13, 2012 - 10:06:19 AM
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DTIM1041.1291040181.pdf
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