941 resultados para RESOLUTION
Resumo:
Up to now, high-resolution mapping of surface water extent from satellites has only been available for a few regions, over limited time periods. The extension of the temporal and spatial coverage was difficult, due to the limitation of the remote sensing technique e.g., the interaction of the radiation with vegetation or cloud for visible observations or the temporal sampling with the synthetic aperture radar (SAR)]. The advantages and the limitations of the various satellite techniques are reviewed. The need to have a global and consistent estimate of the water surfaces over long time periods triggered the development of a multi-satellite methodology to obtain consistent surface water all over the globe, regardless of the environments. The Global Inundation Extent from Multi-satellites (GIEMS) combines the complementary strengths of satellite observations from the visible to the microwave, to produce a low-resolution monthly dataset () of surface water extent and dynamics. Downscaling algorithms are now developed and applied to GIEMS, using high-spatial-resolution information from visible, near-infrared, and synthetic aperture radar (SAR) satellite images, or from digital elevation models. Preliminary products are available down to 500-m spatial resolution. This work bridges the gaps and prepares for the future NASA/CNES Surface Water Ocean Topography (SWOT) mission to be launched in 2020. SWOT will delineate surface water extent estimates and their water storage with an unprecedented spatial resolution and accuracy, thanks to a SAR in an interferometry mode. When available, the SWOT data will be adopted to downscale GIEMS, to produce a long time series of water surfaces at global scale, consistent with the SWOT observations.
Resumo:
We propose a completely automatic approach for recognizing low resolution face images captured in uncontrolled environment. The approach uses multidimensional scaling to learn a common transformation matrix for the entire face which simultaneously transforms the facial features of the low resolution and the high resolution training images such that the distance between them approximates the distance had both the images been captured under the same controlled imaging conditions. Stereo matching cost is used to obtain the similarity of two images in the transformed space. Though this gives very good recognition performance, the time taken for computing the stereo matching cost is significant. To overcome this limitation, we propose a reference-based approach in which each face image is represented by its stereo matching cost from a few reference images. Experimental evaluation on the real world challenging databases and comparison with the state-of-the-art super-resolution, classifier based and cross modal synthesis techniques show the effectiveness of the proposed algorithm.
Resumo:
The distribution of cortical bone in the proximal femur is believed to be a critical component in determining fracture resistance. Current CT technology is limited in its ability to measure cortical thickness, especially in the sub-millimetre range which lies within the point spread function of today's clinical scanners. In this paper, we present a novel technique that is capable of producing unbiased thickness estimates down to 0.3mm. The technique relies on a mathematical model of the anatomy and the imaging system, which is fitted to the data at a large number of sites around the proximal femur, producing around 17,000 independent thickness estimates per specimen. In a series of experiments on 16 cadaveric femurs, estimation errors were measured as -0.01+/-0.58mm (mean+/-1std.dev.) for cortical thicknesses in the range 0.3-4mm. This compares with 0.25+/-0.69mm for simple thresholding and 0.90+/-0.92mm for a variant of the 50% relative threshold method. In the clinically relevant sub-millimetre range, thresholding increasingly fails to detect the cortex at all, whereas the new technique continues to perform well. The many cortical thickness estimates can be displayed as a colour map painted onto the femoral surface. Computation of the surfaces and colour maps is largely automatic, requiring around 15min on a modest laptop computer.
Resumo:
A finite compact (FC) difference scheme requiring only bi-diagonal matrix inversion is proposed by using the known high-resolution flux. Introducing TVD or ENO limiters in the numerical flux, several high-resolution FC-schemes of hyperbolic conservation law are developed, including the FC-TVD, third-order FC-ENO and fifth-order FC-ENO schemes. Boundary conditions formulated need only one unknown variable for third-order FC-ENO scheme and two unknown variables for fifth-order FC-ENO scheme. Numerical test results of the proposed FC-scheme were compared with traditional TVD, ENO and WENO schemes to demonstrate its high-order accuracy and high-resolution.
Resumo:
Recently ZnO nanowire films have been used in very promising and inexpensive dye-sensitized solar cells (DSSC). It was found that the performance of the devices can be enhanced by functionalising the nanowires with a thin metal oxide coating. This nm-scale shell is believed to tailor the electronic structure of the nanowire, and help the absorption of the dye. Core-shell ZnO nanowire structures are synthesised at low temperature (below 120°C) by consecutive hydrothermal growth steps. Different materials are investigated for the coating, including Mg, Al, Cs and Zr oxides. High resolution TEM is used to characterise the quality of both the nanowire core and the shell, and to monitor the thickness and the degree of crystallisation of the oxide coating. The interface between the nanowire core and the outer shell is investigated in order to understand the adhesion of the coating, and give valuable feedback for the synthesis process. Nanowire films are packaged into dye-sensitised solar cell prototypes; samples coated with ZrO2 and MgO show the largest enhancement in the photocurrent and open-circuit voltage and look very promising for further improvement. © 2010 IOP Publishing Ltd.
Restoration of images and 3D data to higher resolution by deconvolution with sparsity regularization