A self-adaptive Hidden Markov Model (SA-HMM) based framework is proposed for behavior recognition in this paper. In this model, if an unknown sequence cannot be classified into an...
Obtaining performance models, like Markov chains and queueing networks, for systems of significant complexity and magnitude is a difficult task that is usually tackled using human...
In this paper we propose a generative statistical approach for the three dimensional (3D) extraction of the branching structure of unfoliaged deciduous trees from urban image seque...
We present efficient sensitivity-analysis algorithms for two problems involving Markov models of sequence evolution: ancestral reconstruction in evolutionary trees and local ungap...
This paper proposes a unified framework for spatiotemporal segmentation of video sequences. A Bayesian network is presented to model the interactions among the motion vector field...