Abstract. Artificial systems with a high degree of autonomy require reliable semantic information about the context they operate in. State interpretation, however, is a difficult ...
Daniel Meyer-Delius, Christian Plagemann, Georg vo...
Abstract. Clustering has recently enjoyed progress via spectral methods which group data using only pairwise affinities and avoid parametric assumptions. While spectral clustering ...
In this paper the acoustic event detection and classification system that has been developed at Athens Information Technology is presented. This system relies on the use of severa...
Activity recognition is a key component for creating intelligent, multi-agent systems. Intrinsically, activity recognition is a temporal classification problem. In this paper, we...
Douglas L. Vail, Manuela M. Veloso, John D. Laffer...
In this paper we describe an artistic exhibition that took place in our highly-immersive virtual-reality laboratory. We have allowed visitors to explore a virtual landscape based o...
This paper presents a new framework for shape modeling and analysis. A shape instance is described by a curvature-based shape descriptor. A Profile Hidden Markov Model (PHMM) is ...
— This paper describes an improved methodology for human motion recognition and imitation based on Factorial Hidden Markov Models (FHMM). Unlike conventional Hidden Markov Models...
Contextual information is important for sequence modeling. Hidden Markov Models (HMMs) and extensions, which have been widely used for sequence modeling, make simplifying, often u...
In this paper we propose the integration of Data Mining with Hidden Markov Models when applied to the problem of acoustic bird species recognition. We first show how each of them...
Erika Vilches, Ivan A. Escobar, Edgar E. Vallejo, ...
In this paper we present a new multiple classifier system (MCS) for recognizing notes written on a whiteboard. This MCS combines one off-line and two on-line handwriting recognit...