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We present a new method for approximate inference in Switching linear Gaussian State Space Models (also known as Switching Kalman Filters. The method is similar in spirit to the Rauch-Tung-Striebel smoother in the Kalman Filter case. Only a single Forward ...
Mixture models form the essential basis of data clustering within a statistical framework. Here, the estimation of the parameters of a mixture of Gaussian densities is considered. In this particular context, it is well known that the maximum likelihood app ...
Tracking speakers in multi-party conversations represents an important step towards automatic analysis of meetings. In this paper, we present a probabilistic method for audio-visual (AV) speaker tracking in a multi-sensor meeting room. The algorithm fuses ...
Tracking speakers in multi-party conversations represents an important step towards automatic analysis of meetings. In this paper, we present a probabilistic method for audio-visual (AV) speaker tracking in a multi-sensor meeting room. The algorithm fuses ...
In this paper we focus on observability for hybrid systems in the mixed-logic dynamical form. We show that the maximal set of observable states, that is usually non convex and disconnected, can be represented as the union of finitely many polytopic regions ...
In this paper, we propose a robust filtering approach to distributed power allocation. A robust filter is used to predict the channel gain and interference under incomplete knowledge of the shadowing coefficient. Simulation results show the superior perfor ...
The standard Kalman filter is a powerful and widely used tool to perform prediction, filtering and smoothing in the fields of linear Gaussian state-space models. In its standard setting it has a simple recursive form which implies high computational effici ...
Particle filters are now established as the most popular method for visual tracking. Within this framework, it is generally assumed that the data are temporally independent given the sequence of object states. In this paper, we argue that in general the da ...
It has been shown previously that systems based on local features and relatively complex generative models, namely 1D Hidden Markov Models (HMMs) and pseudo-2D HMMs, are suitable for face recognition (here we mean both identification and verification). Rec ...
Particle filters are now established as the most popular method for visual tracking. Within this framework, it is generally assumed that the data are temporally independent given the sequence of object states. In this paper, we argue that in general the da ...
