Apr 6, 2017 · In this paper, we consider the problem of modeling and classifying video sequences of dynamic scenes which could be modeled in a dynamic ...

Abstract—Video representation is an important and chal- lenging task in the computer vision community. In this paper, we consider the problem of modeling ...

This note presents an estimator-based fault-tolerant control design approach for a class of linear quantum stochastic systems subject to fault signals. In this ...

This paper proposes a sparse coding framework, named joint video dictionary learning (JVDL), to model a video adaptively, and demonstrates the strong ...

Our algorithm uses a practical implementation of the Rauch-Tung-Striebel (RTS) smoother based on noncausal prediction that models the blurred image as a finite ...

Bibliographic details on Dynamical Textures Modeling via Joint Video Dictionary Learning.

We propose a sparse coding framework, named adaptive video dictionary learning (AVDL), to model a video adaptively. The developed framework is able to capture ...

We propose a sparse coding framework, named adaptive video dictionary learning (AVDL), to model a video adaptively. The developed framework is able to capture ...

This chapter describes how the class of linear dynamical system (LDS) models can be used for representing and analyzing video signals.

Dynamic textures are sequences of images of moving scenes that exhibit certain stationarity properties in time; these include sea-waves, smoke, foliage, ...