Surface Geometric and Electronic Structure of BaFe2As2(001)

BaFe2As2 exhibits properties characteristic of the parent compounds of the newly discovered iron (Fe)-based high-TC superconductors. By combining the real space imaging of scanning tunneling microscopy/spectroscopy (STM/S) with momentum space quantitative Low Energy Electron Diffraction (LEED) we have identified the surface plane of cleaved BaFe2As2 crystals as the As terminated Fe-As layer - the plane where superconductivity occurs. LEED and STM/S data on the BaFe2As2(001) surface indicate an ordered arsenic (As) - terminated metallic surface without reconstruction or lattice distortion. It is surprising that the STM images the different Fe-As orbitals associated with the orthorhombic structure, not the As atoms in the surface plane.Comment: 12 pages, 4 figure

Preferred Spatial Frequencies for Human Face Processing Are Associated with Optimal Class Discrimination in the Machine

Psychophysical studies suggest that humans preferentially use a narrow band of low spatial frequencies for face recognition. Here we asked whether artificial face recognition systems have an improved recognition performance at the same spatial frequencies as humans. To this end, we estimated recognition performance over a large database of face images by computing three discriminability measures: Fisher Linear Discriminant Analysis, Non-Parametric Discriminant Analysis, and Mutual Information. In order to address frequency dependence, discriminabilities were measured as a function of (filtered) image size. All three measures revealed a maximum at the same image sizes, where the spatial frequency content corresponds to the psychophysical found frequencies. Our results therefore support the notion that the critical band of spatial frequencies for face recognition in humans and machines follows from inherent properties of face images, and that the use of these frequencies is associated with optimal face recognition performance

Optimal Y-U-V Model Based On Karhunen-Loeve

An optimal Y-U-V transformation based on Karhunen-Loeve transformation for image compression proposed in this paper is considered as a spectral redundancy reduction. The PSNR is gained for optimal Y-U-V in comparison with traditional fixed Y-U-V transformation, because the variances are most separately after K-L transformation and the down sampling is taken on the coordinates with smallest variances in optimal Y-U-V transformation. The K-L transformation in optimal Y-U-V is an image-dependent transform that is to de-correlate the data in color spectral domain by using eigenvector matrix of covariance of the colors of the image. A normalization matrix follows the optimal Y-U-V transformation is used for ranging the data of Y-U-V within 0-255. The procedure is used in encoding only and the Y-U-V transformation can be transmitted with compressed data and de-coding easily

Maximun Likelihood Clustering Method Based On Color Features

A novel maximum likelihood estimation based on features for color image pixels clustering is proposed. A 3-D color feature data space is spanned to show the pixel frequency of color R, G and B. The clustering based on features can be considered as normal mixture distribution parameter estimation by using maximum likelihood estimation based on features instead of on samples is proposed. An image based on features means that the image is presented by empirical distribution function or special histogram, which includes all possible colors even some color features are absent. It is implied that an IDD (Independent and Identically Distributed) is assumed, The empirical distribution function is formed by Bernoulli random variable. It is shown that the statistics of maximum likelihood estimation based on features are better, because these statistics are sufficient and complete. The clustering based on features can be used for multi-dimensional and multi-class histogram parameter estimation

A Classification Method of Diagnostic Image based on Tangent Distance

In this paper we present a classification approach based on tangent distance, which is specially designed for many applications of image recognition, and discuss the application in our classification of radiographs. In contrast to the traditional method to determine tangent vectors, we design a systemic approach using the single value decomposition to extract the main changed characters of a certain image set. With this new method we achieved considerable results in different image sets including diagnostic images and successful application systems have been developed in practice