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Efficient region tracking with parametric models of geometry and illumination

by Gregory D. Hager, Peter N. Belhumeur - PAMI , 1998
"... 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 v ..."
Abstract - Cited by 563 (30 self) - Add to MetaCart
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.

High Performance Parametric Modeling with Nimrod/G: Killer Application for the Global Grid?

by David Abramson, Jon Giddy, Lew Kotler , 2000
"... 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 ..."
Abstract - Cited by 283 (54 self) - Add to MetaCart
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

multcomp: Simultaneous Inference in General Parametric Models,

by Torsten Hothorn , Frank Bretz , Peter Westfall , 2008
"... Abstract Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the pre-specified significance level. Simultaneous inference procedures have to be ..."
Abstract - Cited by 234 (6 self) - Add to MetaCart
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

Parametric Modeling

by M. M. Ettefagh, M. H. Sadeghi, S. Khanmohammadi
"... ch ive of ..."
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Abstract not found

Dynamic Conditional Correlation: A simple class of multivariate Generalized Autoregressive Conditional Heteroskedasticity Models.

by Robert Engle - Journal of Business & Economic Statistics , 2002
"... Abstract Time varying correlations are often estimated with Multivariate Garch models that are linear in squares and cross products of the data. A new class of multivariate models called dynamic conditional correlation (DCC) models is proposed. These have the flexibility of univariate GARCH models ..."
Abstract - Cited by 711 (17 self) - Add to MetaCart
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.

PARAMETRIC MODELLING FOR MODULAR PREFABRICATED

by Dongki Chung, Seongjun Park, Changsu Shim, Kibong Kim
"... Standardized modular bridges improve the productivity of automation in design and fabrication in response to the variety of influence factors. In this study, optimized design models through the development process of a new structural system were constructed using Building Information Modeling (BIM) ..."
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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

Identification in Parametric Models IDENTIFICATION IN PARAMETRIC MODELS

by Thomas J Rothenberg Reviewed , Thomas J Rothenberg'
"... JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about J ..."
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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

Generalized Autoregressive Conditional Heteroskedasticity

by Tim Bollerslev - JOURNAL OF ECONOMETRICS , 1986
"... A natural generalization of the ARCH (Autoregressive Conditional Heteroskedastic) process introduced in Engle (1982) to allow for past conditional variances in the current conditional variance equation is proposed. Stationarity conditions and autocorrelation structure for this new class of parametri ..."
Abstract - Cited by 2406 (30 self) - Add to MetaCart
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.

Persistent Naming for Parametric Models

by Dago Agbodan, David Marcheix, Guy Pierra - in WSCG’2000, Vol , 2000
"... 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-evaluate ..."
Abstract - Cited by 4 (3 self) - Add to MetaCart
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

Neuro-Wavelet Parametric Modeling

by Colla Reyneri Sgarbi, V. Colla, L. M. Reyneri, M. Sgarbi
"... 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 appli ..."
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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
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