165 resultados para Moving images


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(1x1) and (2x1) reconstructions of the (001) SrTiO3 surface were studied using the first-principles full-potential linear muffin-tin orbital method. Surface energies were calculated as a function of TiO2 chemical potential, oxygen partial pressure and temperature. The (1x1) unreconstructed surfaces were found to be energetically stable for many of the conditions considered. Under conditions of very low oxygen partial pressure the (2x1) Ti2O3 reconstruction [Martin R. Castell, Surf. Sci. 505, 1 (2002)] is stable. The question as to why STM images of the (1x1) surfaces have not been obtained was addressed by calculating charge densities for each surface. These suggest that the (2x1) reconstructions would be easier to image than the (1x1) surfaces. The possibility that the presence of oxygen vacancies would destabilise the (1x1) surfaces was also investigated. If the (1x1) surfaces are unstable then there exists the further possibility that the (2x1) DL-TiO2 reconstruction [Natasha Erdman Nature (London) 419, 55 (2002)] is stable in a TiO2-rich environment and for p(O2)>10(-18) atm.

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In a typical shoeprint classification and retrieval system, the first step is to segment meaningful basic shapes and patterns in a noisy shoeprint image. This step has significant influence on shape descriptors and shoeprint indexing in the later stages. In this paper, we extend a recently developed denoising technique proposed by Buades, called non-local mean filtering, to give a more general model. In this model, the expected result of an operation on a pixel can be estimated by performing the same operation on all of its reference pixels in the same image. A working pixel’s reference pixels are those pixels whose neighbourhoods are similar to the working pixel’s neighbourhood. Similarity is based on the correlation between the local neighbourhoods of the working pixel and the reference pixel. We incorporate a special instance of this general case into thresholding a very noisy shoeprint image. Visual and quantitative comparisons with two benchmarking techniques, by Otsu and Kittler, are conducted in the last section, giving evidence of the effectiveness of our method for thresholding noisy shoeprint images.

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This paper introduces a fast algorithm for moving window principal component analysis (MWPCA) which will adapt a principal component model. This incorporates the concept of recursive adaptation within a moving window to (i) adapt the mean and variance of the process variables, (ii) adapt the correlation matrix, and (iii) adjust the PCA model by recomputing the decomposition. This paper shows that the new algorithm is computationally faster than conventional moving window techniques, if the window size exceeds 3 times the number of variables, and is not affected by the window size. A further contribution is the introduction of an N-step-ahead horizon into the process monitoring. This implies that the PCA model, identified N-steps earlier, is used to analyze the current observation. For monitoring complex chemical systems, this work shows that the use of the horizon improves the ability to detect slowly developing drifts.

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Two sequences of solar images obtained by the Transition Region and Coronal Explorer in three UV passbands are studied using wavelet and Fourier analysis and compared to the photospheric magnetic flux measured by the Michelson Doppler Interferometer on the Solar Heliospheric Observatory to study wave behavior in differing magnetic environments. Wavelet periods show deviations from the theoretical cutoff value and are interpreted in terms of inclined fields. The variation of wave speeds indicates that a transition from dominant fast-magnetoacoustic waves to slow modes is observed when moving from network into plages and umbrae. This implies preferential transmission of slow modes into the upper atmosphere, where they may lead to heating or be detected in coronal loops and plumes.