973 resultados para Multi-View Rendering


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Artist's rendering of an aerial view of the stadium.

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"This report was prepared by University of Florida, Gainesville, Florida, under USAF Contract no. F 33615-67-C-1227."

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"September 1996."

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Online geographic information systems provide the means to extract a subset of desired spatial information from a larger remote repository. Data retrieved representing real-world geographic phenomena are then manipulated to suit the specific needs of an end-user. Often this extraction requires the derivation of representations of objects specific to a particular resolution or scale from a single original stored version. Currently standard spatial data handling techniques cannot support the multi-resolution representation of such features in a database. In this paper a methodology to store and retrieve versions of spatial objects at, different resolutions with respect to scale using standard database primitives and SQL is presented. The technique involves heavy fragmentation of spatial features that allows dynamic simplification into scale-specific object representations customised to the display resolution of the end-user's device. Experimental results comparing the new approach to traditional R-Tree indexing and external object simplification reveal the former performs notably better for mobile and WWW applications where client-side resources are limited and retrieved data loads are kept relatively small.

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We introduce a new second-order method of texture analysis called Adaptive Multi-Scale Grey Level Co-occurrence Matrix (AMSGLCM), based on the well-known Grey Level Co-occurrence Matrix (GLCM) method. The method deviates significantly from GLCM in that features are extracted, not via a fixed 2D weighting function of co-occurrence matrix elements, but by a variable summation of matrix elements in 3D localized neighborhoods. We subsequently present a new methodology for extracting optimized, highly discriminant features from these localized areas using adaptive Gaussian weighting functions. Genetic Algorithm (GA) optimization is used to produce a set of features whose classification worth is evaluated by discriminatory power and feature correlation considerations. We critically appraised the performance of our method and GLCM in pairwise classification of images from visually similar texture classes, captured from Markov Random Field (MRF) synthesized, natural, and biological origins. In these cross-validated classification trials, our method demonstrated significant benefits over GLCM, including increased feature discriminatory power, automatic feature adaptability, and significantly improved classification performance.

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The advantages of antennas that can resemble the shape of the body to which they are attached are obvious. However, electromagnetic modeling of such unusually shaped antennas can be difficult. In this paper, the commercially available software SolidWorks(TM) is used for accurately drawing complex shapes in conjunction with the electromagnetic software FEKO(TM) to model the EM behavior of conformal antennas. The application of SolidWorks and custom-written software allows all the required information that forms the analyzed structure to be automatically inserted into FEKO, and gives the user complete control over the antenna being modeled. This approach is illustrated by a number of simulation examples of single, wideband, multi-band planar and curved patch antennas.

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Multi-layer hydrogen storage thin films with Mg and MmNi(3.5)(CoAlMn)(1.5) (here Mm denotes La-rich mischmetal) as alternative layers were prepared by direct current magnetron sputtering. Transmission electron microscopy investigation shows that the microstructure of the MmNi(3.5)(CoAlMn)(1.5) and Mg layers are significantly different although their deposition conditions are the same. The MmNi(3.5)(CoAlMn)(1.5) layer is composed of two regions: one is an amorphous region approximately 4 nm thick at the bottom of the layer and the other is a nanocrystalline region on top of the amorphous region. The Mg layer is also composed of two regions: one is a randomly orientated nanocrystalline region 50 nm thick at the bottom of the layer and the other is a columnar crystallite region on top of the nanocrystalline region. These Mg columnar crystallites have their [001] directions parallel to the growth direction and the average lateral size of these columnar crystallites is about 100 nm. A growth mechanism of the multi-layer thin films is discussed based on the experiment results. Wiley-Liss, Inc.