In this paper, we present an event parsing algorithm based on Stochastic Context Sensitive Grammar (SCSG) for understanding events, inferring the goal of agents, and predicting th...
Mingtao Pei, School of Computer Science, Yunde Jia...
This paper addresses online learning of reference object distribution in the context of two hybrid tracking schemes that combine the mean shift with local point feature correspond...
We examine a general Bayesian framework for constructing on-line prediction algorithms in the experts setting. These algorithms predict the bits of an unknown Boolean sequence usin...
Our aim in this paper is to develop a Bayesian framework for matching hierarchical relational models. Such models are widespread in computer vision. The framework that we adopt fo...
In multiagent environments, forms of social learning such as teaching and imitation have been shown to aid the transfer of knowledge from experts to learners in reinforcement lear...
In this paper we propose a Bayesian framework for XCS [9], called BXCS. Following [4], we use probability distributions to represent the uncertainty over the classifier estimates ...
Davide Aliprandi, Alex Mancastroppa, Matteo Matteu...
In this paper we present a framework for a navigation system in an indoor environment using only omnidirectional video. Within a Bayesian framework we seek the appropriate place a...
This paper presents a Bayesian framework for 3D facial reconstruction. The framework iteratively deforms a generic face mesh to fit a set of range points representing a face. The...
Image attention is the basic technique for many computer vision applications. In this paper, we propose an adaptive Bayesian framework to detect the image attention in color image...