#### Filter Results:

- Full text PDF available (10)

#### Publication Year

1997

2010

- This year (0)
- Last 5 years (0)
- Last 10 years (2)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Key Phrases

Learn More

- Péter Torma, Csaba Szepesvári
- AISTATS
- 2003

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 sampling/resampling (SIR) algorithm may yield high variance estimates of the posterior in this case, resulting in… (More)

- Péter Torma, Csaba Szepesvári
- ECCV
- 2004

Particle filters provide a means to track the state of an object even when the dynamics and the observations are non-linear/nonGaussian. 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)

- Alexis Grosofsky, Sarah Adkins, +4 authors Peter Torma
- Perceptual and motor skills
- 2003

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)

- P. Torma, C. Szepesvari
- ISPA 2005. Proceedings of the 4th International…
- 2005

An unsatisfactory property of particle filters is that they may become inefficient when the observation noise is low. In this paper we consider a simple-to-implement particle filter, called 'LIS-based particle filter', whose aim is to overcome the above mentioned weakness. LIS-based particle filters sample the particles in a two-stage process that uses… (More)

- Péter Torma, Csaba Szepesvári
- Journal of Multimedia
- 2006

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)

LS-N-IPS (Local-Search-N-Interacting-Particle-System) is an extension of the standard N-IPS 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)

- Péter Torma, András György, Csaba Szepesvári
- AISTATS
- 2010

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)

- W Lange, Q A Turchette, +44 authors A Tapp
- 1997

Recently Quantum Computation has generated a lot of interest due to the discovery of a quantum algorithm which can factor large numbers in polynomial time. The usefulness of a quantum computer is limited by the effect of errors. Simulation is a useful tool for determining the feasibility of quantum computers in the presence of errors. The size of a quantum… (More)

- Peter Torma
- 2001

A modification of N-IPS, a well known parti cle filter method is proposed and is shown to be more efficient than the baseline algorithm in the small sample size limit and when the observations are "reliable". The algorithm called LS-N-IPS adds local search to the base line algorit.hm: in each time step the predic tions arc refined in a local search… (More)

A recently introduced particle filtering method, called LS-N-IPS, is considered for tracking objects on video sequences. LS-N-IPS is a computationally efficient particle filter that performs better than the standard N-IPS particle filter, when observations are highly peaky, as it is the case of visual object tracking problems with good observation models.… (More)