Péter Torma

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A Markov-chain Monte Carlo based algorithm is provided to solve the Simultaneous localization and mapping (SLAM) problem with general dynamics and observation model under open-loop control and provided that the map-representation is finite dimensional. To our knowledge this is the first provably consistent yet (close-to) practical solution to this problem.(More)
We consider the task of filtering dynamical systems observed in noise by means of sequential importance sampling when the proposal is restricted to the innovation components of the state. It is argued that the unmodified sequential importance sam-pling/resampling (SIR) algorithm may yield high variance estimates of the posterior in this case, resulting in(More)
Particle filters provide a means to track the state of an object even when the dynamics and the observations are non-linear/non-Gaussian. However, they can be very inefficient when the observation noise is low as compared to the system noise, as it is often the case in visual tracking applications. In this paper we propose a new two-stage sampling procedure(More)
Simulating a quantum computer requires vast computational and processing resources due to the exponential nature of quantum mechanics. Simulating a detailed model like the Cirac and Zoller trapped ion scheme adds further to this complexity. In this paper we define a less complex model which accurately models the trapped ion quantum computer. This model(More)
LS-NIPS (Local-Search-N-Interacting-Particle-System) is an extension of the standard NIPS particle filter (also known as CONDENSATION in the image processing literature). The modified algorithm adds local search to the baseline algorithm: in each time step the predictions are refined in a local search procedure that utilizes the most recent observation. A(More)
– In the low observation noise limit particle filters become inefficient. In this paper a simple-to-implement particle filter is suggested as a solution to this well-known problem. The proposed Local Importance Sampling based particle filters draw the particles' positions in a two-step process that makes use of both the dynamics of the system and the most(More)
A recently introduced particle filtering method, called LS-NIPS , is considered for tracking objects on video sequences. LS-NIPS is a computationally efficient particle filter that performs better than the standard NIPS particle filter, when observations are highly peaky, as it is the case of visual object tracking problems with good observation models. An(More)
Tooth whitening has become a very popular procedure. Advertisements for whitening products imply that whiter teeth are more attractive than yellower teeth. We tested this idea empirically by manipulating the tooth color of pictures of male and female targets. Participants' ratings of attractiveness were not influenced by tooth color. Exp. 2 yielded a(More)
This paper presents a novel facial-pose tracking algorithm using LS-NIPS (Local Search N-Interacting Particle System), an algorithm that has been introduced recently by the authors. LS-NIPS is a probabilistic tracking algorithm that keeps track of a number of alternative hypotheses at any time, the particles. LS-NIPS has three components: a dynamical model,(More)
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