Publications associées à Embedding Motion in Model-Based Stochastic Tracking

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

Sparse Sampling of Signal Innovations

Martin Vetterli, Pina Marziliano, Thierry Blu, Lionel Coulot, Pier Luigi Dragotti

Sparse sampling of continuous-time sparse signals is addressed. In particular, it is shown that sampling at the rate of innovation is possible, in some sense applying Occam's razor to the sampling of sparse signals. The noisy case is analyzed and solved, p ...

Institute of Electrical and Electronics Engineers2008

Motion Modeling: Can We Get Rid of Motion Capture?

Daniel Thalmann

For situations like crowd simulation, serious games, and VR-based training, flexible and spontaneous movements are extremely important. Motion models would be the best strategy to adopt, but unfortunately, they are very costly to develop and the results ar ...

Springer-Verlag New York, Ms Ingrid Cunningham, 175 Fifth Ave, New York, Ny 10010 Usa2008

Differences between observation and sampling error in sparse signal reconstruction

Michael Christoph Gastpar

The field of Compressed Sensing has shown that a relatively small number of random projections provide sufficient information to accurately reconstruct sparse signals. Inspired by applications in sensor networks in which each sensor is likely to observe a ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2007

Embedding Motion in Model-Based Stochastic Tracking

Daniel Gatica-Perez, Jean-Marc Odobez, Silèye Oumar Ba

Particle filtering (PF) is now established as one of the most popular methods for visual tracking. Within this framework, two assumptions are generally made. The first is that the data are temporally independent given the sequence of object states, and the ...

2006

Sensing and communication with and without bits

Martin Vetterli, Michael Christoph Gastpar, Pier Luigi Dragotti

The successful design of sensor network architectures depends crucially on the structure of the sampling, observation, and communication processes. One of the most fundamental ques- tions concerns the sufficiency of discrete approximations in time, space, ...

2006

Improved echo volumar imaging (EVI) for functional MRI

Wietske Van der Zwaag

Echo volumar imaging (EVI) is a 3D modification of echo-planar imaging (EPI) that allows data from an entire volume to be acquired following a single RF excitation. EVI provides a high rate of volumar data acquisition, which is advantageous for functional ...

Wiley-Blackwell2006

The plenacoustic function on the circle with application to HRTF interpolation

Martin Vetterli, Luciano Sbaiz, Thibaut Ajdler

The sampling of the soundfield using circular microphone arrays is studied. We give a new representation of the soundfield sampled on such an array using the two-dimensional Fourier transform. We then show how we can reconstruct the soundfield at all possi ...

2005

Motion likelihood and proposal modeling in Model-Based Stochastic Tracking

Daniel Gatica-Perez, Jean-Marc Odobez

Particle filtering is now established as one of the most popular methods for visual tracking. Within this framework, there are two important considerations. The first one refers to the generic assumption that the observations are temporally independent giv ...

IDIAP2004

Embedding Motion in Model-Based Stochastic Tracking

Graph Chatbot

Chattez avec Graph Search

Sparse Sampling of Signal Innovations

Motion Modeling: Can We Get Rid of Motion Capture?

Differences between observation and sampling error in sparse signal reconstruction

Embedding Motion in Model-Based Stochastic Tracking

Sensing and communication with and without bits

Motion models for robust 3D human body tracking

Improved echo volumar imaging (EVI) for functional MRI

The plenacoustic function on the circle with application to HRTF interpolation

Ultrawide Bandwidth Signals as Shot-Noise: a Unifying Approach

Motion likelihood and proposal modeling in Model-Based Stochastic Tracking

Sparse Sampling of Signal Innovations

Motion Modeling: Can We Get Rid of Motion Capture?

Ultrawide Bandwidth Signals as Shot-Noise: a Unifying Approach

Differences between observation and sampling error in sparse signal reconstruction

Embedding Motion in Model-Based Stochastic Tracking

Sensing and communication with and without bits

The plenacoustic function on the circle with application to HRTF interpolation

Improved echo volumar imaging (EVI) for functional MRI

Motion models for robust 3D human body tracking

Motion likelihood and proposal modeling in Model-Based Stochastic Tracking