This paper presents a new object-based segmentation technique which exploits a large temporal context in order to get coherent and robust segmentation results. The segmentation process is seen as a problem of minimization of an energy function. This energy function takes into account a data attach term and spatial and temporal regularization terms. The proposed technique used to minimize this e...

We present an efficient video segmentation and tracking strategy based on edge information to assist object-based video coding, motion estimation, and motion compensation for MPEG-4 and MPEG-7. The proposed algorithm utilizes the human visual perception to provide edge information. Three parameters are introduced and described based on edge information from the analysis of a local histogram. An...

This paper deals with motion estimation of an airborne camera and generation of 3D landscape models from image sequences for object-based video coding. While most camera motion estimators use a random sampling outlier elimination, the presented new approach uses a priori knowledge to simplify the complexity of the outlier elimination and to achieve real time coding. Test results with synthetic ...

Delay time modelling (DTM) is the process to establish the mathematical model based on the delay time concept and then to use it for improving plant maintenance management. The delay time model can be divided into a single component model (component-tracking model) or a complex system model (pooled-components model). DTM has been proved to be a methodology readily embraced by engineers for mode...

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...

Relevance feedback is a powerful technique for content-based image retrieval. Many parameter estimation approaches have been proposed for relevance feedback. However, most of them have only utilized information of the relevant retrieved images, and have given up, or have not made great use of information of the irrelevant retrieved images. This paper presents a novel approach to update the inte...

The paper derives a 2-Dimensional spectrum estimator from some recent results on the statistical properties of wavelet packet coefficients of random processes. It provides an analysis of the bias of this estimator with respect to the wavelet order. The paper also discusses the performance of this wavelet based estimator, in comparison with the conventional 2-D Fourier-based spectrum estimator o...

This paper considers the problem of distributed adaptive linear parameter estimation in multiagent inference networks. Local sensing model information is only partially available at the agents, and interagent communication is assumed to be unpredictable. The paper develops a generic mixed time-scale stochastic procedure consisting of simultaneous distributed learning and estimation, in which th...

Estimation by analogy EBA (effort estimation by analogy) is one of the proven methods for effort prediction in software engineering; in AI this would be called Case-Based Reasoning. In this paper we consider effort predictions using the EBA () method AQUA and pay attention to two aspects: (i) The influence of the set of analogs on the quality of prediction. The set of analogs is determined by a...

We use weekly survey data on short-term and medium-term sentiment of German investors to estimate the parameters of a stochastic model of opinion dynamics. The bivariate nature of our data set also allows us to explore the interaction between the two hypothesized opinion formation processes, while consideration of the simultaneous weekly changes of the stock index DAX enables us to study the in...