EEcient access to information contained in video databases implies that a struc-tured representation of the content of the video is built beforehand. This paper describes an approach in this direction, targeted at video indexing and browsing. Exploiting a 2D motion model estimator, we partition the video into shots, characterize camera motion, extract and track mobile objects. These steps rely ...

Pattern-based video coding representing moving regions in macroblock has very good potential for improved coding efficiency over existing standard H.264/AVC in the very low bit-rate range. However, the coding efficiency diminishes compared to H.264 with the bit rates because variable block sizes of H.264 for motion estimation and compensation are successfully selected for any bit rates using we...

In what concerns extreme values modeling, heavy tailed autoregressive processes defined with the minimum or maximum operator have proved to be good alternatives to classical linear ARMA with heavy tailed marginals (Davis and Resnick [8], Ferreira and Canto e Castro [13]). In this paper we present a complete characterization of the tail behavior of the autoregressive Pareto process known as Yeh–...

In this paper, we investigate preamble designs for channel estimation, that jointly address the estimation efficiency in terms of mean squared error (MSE) of the channel estimates, and the out of band (OOB) radiation of the transmit preambles. We provide two novel design techniques, based on a convex optimization problem, to obtain optimal preambles for a single carrier and provide a juxtaposit...

The first stage in many pattern recognition tasks is to generate a good set of features from the observed data. Usually, only a single feature space is used. However, in some complex pattern recognition tasks the choice of a good feature space may vary depending on the signal content. An example is in speech recognition where phone dependent feature subspaces may be useful. Handling multiple su...

Previous work on statistical language generation has primarily focused on grammaticality and naturalness, scoring generation possibilities according to a language model or user feedback. More recent work has investigated data-driven techniques for controlling linguistic style without overgeneration, by reproducing variation dimensions extracted from corpora. Another line of work has produced ha...

System identification and parameter estimation are important to obtain information from systems which are difficult to model and that are usually presented as BlackBox models. This work presents a point to point parameter estimation of a generalized non-deterministic system, whose results are variable through time, by using an exponential Forgetting Factor (FF). An average approximation is used...

This paper describes a method to estimate parametric motion models. Motivations for the use of such models are on one hand their efficiency, which has been demonstrated in numerous contexts such as estimation, segmentation, tracking and interpretation of motion, and on the other hand, their low computational cost compared to optical flow estimation. However, it is important to have the best acc...

• The last decade has seen development of a plethora of approaches for threshold estimation in extreme value applications. From a statistical perspective, the threshold is loosely defined such that the population tail can be well approximated by an extreme value model (e.g., the generalised Pareto distribution), obtaining a balance between the bias due to the asymptotic tail approximation and p...

This paper presents the analysis of errors produced by block matching motion estimation methods with lower computational cost (fast search algorithms). We try to find an answer on the question what measure can be used to determine correctness of searched motion vectors and which motion vectors are founded as fault (corrupted by noise). We consider the vector fields obtained by using of full sea...