In this paper, we investigate the use of the coupled hidden Markov models (CHMM) for the task of audio-visual text dependent speaker identification. Our system determines the identity of the user from a temporal sequence of audio and visual observations obtained from the acoustic speech and the shape of the mouth, respectively. The multi modal observation sequences are then modeled using a set ...

Automated segmentation of tumors from a multispectral data set like that of the Magnetic Resonance Images (MRI) is challenging. Independent Component Analysis (ICA) and its variations for Blind Source Separation (BSS) have been employed in previous studies but have met with cumbersome obstacles due to its inherent limitations. Here we have approached the multispectral data set initially with fe...

We present a novel methodology for the analysis of complex object shapes in motion observed by multiple video cameras. In particular, we propose to learn local surface rigidity probabilities (i.e., deformations), and to estimate a mean pose over a temporal sequence. Local deformations can be used for rigidity-based dynamic surface segmentation, while a mean pose can be used as a sequence keyfra...

Conventional decision trees have a number of favorable properties, including interpretability, a small computational footprint and the ability to learn from little training data. However, they lack a key quality that has helped fuel the deep learning revolution: that of being end-to-end trainable, and to learn from scratch those features that best allow to solve a given supervised learning prob...

Optimisation of distribution parameters is a very common problem. There are many sorts of distributions which can be used to model environment processes, biological functions or graphical data. However, it is common that parameters of those distribution may be, partially or completely unknown. Mixture models composed of a few distributions are easier to solve. In such a case simple estimation m...

Object tracking is an ubiquitous problem that appears in many applications such as remote sensing, audio processing, computer vision, human-machine interfaces, human-robot interaction, etc. Although thoroughly investigated in computer vision, tracking a time-varying number of persons remains a challenging open problem. In this paper, we propose an on-line variational Bayesian model for multi-pe...

Providers of high quality-of-service over telecommunication networks require accurate methods for remote measurement of link-level performance. Recent research in network tomography has demonstrated that it is possible to estimate internal link characteristics, e.g., link delays and packet losses, using unicast probing schemes in which probes are exchanged between several pairs of sites in the ...

Decentralized partially observable Markov decision processes (Dec-POMDPs) provide a general framework for multiagent sequential decision-making under uncertainty. Although Dec-POMDPs are typically intractable to solve for real-world problems, recent research on macro-actions (i.e., temporally-extended actions) has significantly increased the size of problems that can be solved. However, current...

In this paper we tackle the problem of classifying objects, which are not known to the system but similar to some of the objects contained in the training set. This type of classification is referred to as generic object modeling and recognition and is necessary for applications were it is impossible to model all occurring objects. As no class for unknown objects exist, they are either rejected...

The goal of supervised learning is to infer a functional mapping based on a set of training examples. To achieve good generalization, it is necessary to control the “complexity” of the learned function. In Bayesian approaches, this is done by adopting a prior for the parameters of the function being learned. We propose a Bayesian approach to supervised learning, which leads to sparse solutions;...