Local Binary Pattern (LBP) is a simple yet powerful method for image feature extraction in pattern recognition and image processing. However, the LBP operator of each pixel mainly depends on its neighboring pixels and emphasizes on local information too much. From the practical viewpoint, the information is quite limited if we consider the LBP operator in isolation, especially for a large image. To deal with this issue, we propose ultra LBP (U-LBP), which consider the relationship among different LBP operators. The proposed method cannot only get the local but also ultra local information. The effectiveness of the proposed algorithm is investigated on gender recognition and digit recognition, respectively. The experimental results show that the proposed method outperforms the traditional LBP.

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A large number of coal-fired flue gas is the culprit that caused by air pollution and acid rain. Based on this reason, lime/limestone-gypsum wet flue gas desulfurization(FGD) process as the main desulfurization technology are used by most countries to control emissions of SO 2, at the same time generating a huge amount of flue gas desulfurization gypsum。This paper describes the physical and chemical properties of natural gypsum and FGD gypsum，reviews the utilization of experience and research status and progress of FGD gypsum. Our country FGD gypsum problems in the application process is also pointed out.

SUMMARYChronic liver disease is a major worldwide health problem. Diagnosis and staging of chronic liver diseases is an important issue. In this paper, we propose a quantitative method of analyzing local morphological changes for accurate and practical computer-aided diagnosis of cirrhosis. Our method is based on sparse and low-rank matrix decomposition, since the matrix of the liver shapes can be decomposed into two parts: a low-rank matrix, which can be considered similar to that of a normal liver, and a sparse error term that represents the local deformation. Compared with the previous global morphological analysis strategy based on the statistical shape model (SSM), our proposed method improves the accuracy of both normal and abnormal classifications. We also propose using the norm of the sparse error term as a simple measure for classification as normal or abnormal. The experimental results of the proposed method are better than those of the state-of-the-art SSM-based methods. key words: local morphological analysis, diagnosis of cirrhosis, sparse and low-rank matrix decomposition, augmented lagrange multiplier

The existing works on writer identification consider global feature or local feature, respectively, but not both. Actually, both of global and local features provide the useful information for writer identification. Hence, this paper proposes a method for writer identification by using a mixture of global feature and local feature. In implementation, we utilize 2-D Gabor transformation as the global feature and Local Binary Pattern (LBP) as the local feature for writer identification. The experiment results show that the combination of global and local feature outperforms the utilization of each single one.