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SAFER under vehicle inspection through video mosaic building (2004)

Koschan, Andreas ; Page, David ; Ng, Jin-Choon ; [et al.]

Bradford : Emerald

Industrial robot 31 (2004), S. 435-442

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ISSN: 0143-991X

Source: Emerald Fulltext Archive Database 1994-2005

Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics

Notes: The current threats to US security, both military and civilian, have led to an increased interest in the development of technologies to safeguard national facilities such as military bases, federal buildings, nuclear power plants, and national laboratories. As a result, the imaging, robotics, and intelligent systems (IRIS) laboratory at the University of Tennessee has established a research consortium, known as security automation and future electromotive robotics (SAFER), to develop, test, and deploy sensing and imaging systems. In this paper, we describe efforts made to build multi-perspective mosaics of infrared and color video data for the purpose of under vehicle inspection. It is desired to create a large, high-resolution mosaic that may be used to quickly visualize the entire scene shot by a camera making a single pass underneath the vehicle. Several constraints are placed on the video data in order to facilitate the assumption that the entire scene in the sequence exists on a single plane. Therefore, a single mosaic is used to represent a single video sequence.

Type of Medium: Electronic Resource

URL: http://dx.doi.org/10.1108/01439910410551854

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Variogram Model Selection via Nonparametric Derivative Estimation (2000)

Gorsich, David J. ; Genton, Marc G.

Springer

Mathematical geology 32 (2000), S. 249-270

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ISSN: 1573-8868

Keywords: nonparametric ; variogram fitting ; derivative estimation ; generalized least squares ; model selection ; aliasing

Source: Springer Online Journal Archives 1860-2000

Topics: Geosciences , Mathematics

Notes: Abstract Before optimal linear prediction can be performed on spatial data sets, the variogram is usually estimated at various lags and a parametric model is fitted to those estimates. Apart from possible a priori knowledge about the process and the user's subjectivity, there is no standard methodology for choosing among valid variogram models like the spherical or the exponential ones. This paper discusses the nonparametric estimation of the variogram and its derivative, based on the spectral representation of positive definite functions. The use of the estimated derivative to help choose among valid parametric variogram models is presented. Once a model is selected, its parameters can be estimated—for example, by generalized least squares. A small simulation study is performed that demonstrates the usefulness of estimating the derivative to help model selection and illustrates the issue of aliasing. MATLAB software for nonparametric variogram derivative estimation is available at http://www-math.mit.edu/~gorsich/derivative.html. An application to the Walker Lake data set is also presented.