Abstract—As an object moves through the field of view of a camera, the images of the object may change dramatically. This is not simply due to the translation of the object across the image plane. Rather, complications arise due to the fact that the object undergoes changes in pose relative to the viewing camera, changes in illumination relative to light sources, and may even become partially or fully occluded. In this paper, we develop an efficient, general framework for object tracking—one which addresses each of these complications. We first develop a computationally efficient method for handling the geometric distortions produced by changes in pose. We then combine geometry and illumination into an algorithm that tracks large image regions using no more computation than would be required to track with no accommodation for illumination changes. Finally, we augment these methods with techniques from robust statistics and treat occluded regions on the object as statistical outliers. Throughout, we present experimental results performed on live video sequences demonstrating the effectiveness and efficiency of our methods. Index Terms—Visual tracking, real-time vision, illumination, motion estimation, robust statistics.

This paper examines the role of parametric modeling as an application for the global computing grid, and explores some heuristics which make it possible to specify soft real time deadlines for larger computational experiments. We demonstrate the scheme with a case study utilizing the Globus toolkit

to be used which adjust for multiplicity and thus control the overall type I error rate. In this paper we describe simultaneous inference procedures in general parametric models, where the experimental questions are specified through a linear combination of elemental model parameters. The framework described

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) technologies. The models consider design specifications, constructability and optimized detailing. Parametric modeling was conducted to accommodate variation of each design values of the modular bridge structures such as bridge width, girder spacing and height of the pier. Each module by parametric modeling

coupled with parsimonious parametric models for the correlations. They are not linear but can often be estimated very simply with univariate or two step methods based on the likelihood function. It is shown that they perform well in a variety of situations and provide sensible empirical results.

JSTOR, please contact support@jstor.org. The Econometric Society is collaborating with JSTOR to digitize, preserve and extend access to Econometrica. http://www.jstor.org IDENTIFICATION IN PARAMETRIC MODELS BY THOMAS J. ROTHENBERG' A theory of identification is developed for a general stochastic

Nowadays, many commercial CAD systems support history-based, constraint-based and feature-based modelling. The use of these new capabilities raises the issue of persistent naming which refers to the problem of identifying entities in an initial parametric model and matching them in the re

This work describes Neuro-Wavelet Parametric Modeling, a neural-based technique to classify, model and forecast signals or problems which are functions of either time or space. The paper presents the base method and discusses on the selection of the optimal neuro-wavelet network. An industrial

of parametric models are derived. Maximum likelihood estimation and testing are also considered. Finally an empirical example relating to the uncertainty of the inflation rate is presented.