4,767 research outputs found
A Game-Theoretic Approach to Robust Selection of Multi-view Point Correspondence
In this paper we introduce a robust matching technique that allows very accurate selection of corresponding feature points from multiple views. Robustness is achieved by enforcing global geometric consistency at an early stage of the matching process, without the need of subsequent verification through reprojection. The global consistency is reduced to a pairwise compatibility making use of the size and orientation information provided by common feature descriptors, thus projecting what is a high-order compatibility problem into a pairwise setting. Then a game-theoretic approach is used to select a maximally consistent set of candidate matches, where highly compatible matches are enforced while incompatible correspondences are driven to extinction © 2010 IEEE
Penalty-regulated dynamics and robust learning procedures in games
Starting from a heuristic learning scheme for N-person games, we derive a new
class of continuous-time learning dynamics consisting of a replicator-like
drift adjusted by a penalty term that renders the boundary of the game's
strategy space repelling. These penalty-regulated dynamics are equivalent to
players keeping an exponentially discounted aggregate of their on-going payoffs
and then using a smooth best response to pick an action based on these
performance scores. Owing to this inherent duality, the proposed dynamics
satisfy a variant of the folk theorem of evolutionary game theory and they
converge to (arbitrarily precise) approximations of Nash equilibria in
potential games. Motivated by applications to traffic engineering, we exploit
this duality further to design a discrete-time, payoff-based learning algorithm
which retains these convergence properties and only requires players to observe
their in-game payoffs: moreover, the algorithm remains robust in the presence
of stochastic perturbations and observation errors, and it does not require any
synchronization between players.Comment: 33 pages, 3 figure
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