A new method for expiration code detection and recognition using gabor features based collaborative representation

Text in images and video contains important information for visual content understanding, indexing, and recognizing. Extraction of this information involves preprocessing, localization and extraction of the text from a given image. In this paper, we propose a novel expiration code detection and recognition algorithm by using Gabor features and collaborative representation based classification. The proposed system consists of four steps: expiration code location, character isolation, Gabor features extraction and characters recognition. For expiration code detection, the Gabor energy (GE) and the maximum energy difference (MED) are extracted. The performance of the recognition algorithm is tested over three Gabor features: GE, magnitude response (MR) and imaginary response (IR). The Gabor features are classified based on collaborative representation based classifier (GCRC). To encompass all frequencies and orientations, downsampling and principal component analysis (PCA) are applied in order to reduce the features space dimensionality. The effectiveness of the proposed localization algorithm is highlighted and compared with other existing methods. Extensive testing shows that the suggested detection scheme outperforms existing methods in terms of detection rate for large image database. Also, GCRC show very competitive results compared with Gabor feature sparse representation based classification (GSRC). Also, the proposed system outperforms the nearest neighbor (NN) classifier and the collaborative representation based classification (CRC).     

基于三次 B 样条插值曲面的图像放大方法*

为了实现低分辨率图像的高保真放大,利用原图像中相邻四个像素点的像素值及其在放大后的图像中的位置构造出了对应的三次B样条插值曲面,从而较好地估算出了放大后图像中出现的空白像素值。鉴于彩色图的像素点有R、G、B三个分量,分别构造R曲面、G曲面、B曲面对空白像素点的三个分量进行填充。由于采用构造曲面的高度值作为像素值,使得相邻像素点的像素值不存在较大差异,故而尽可能地避免了马赛克现象的产生,实现了图像的高保真放大。通过对比实验,验证了论文方法对图像放大具有非常好的效果。     

Variance sensitive adaptive threshold-based PCA method for fault detection with experimental application

Principal Component Analysis (PCA) is a statistical process monitoring technique that has been widely used in industrial applications. PCA methods for Fault Detection (FD) use data collected from a steady-state process to monitor T² and Q statistics with a fixed threshold. For the systems where transient values of the processes must be taken into account, the usage of a fixed threshold in PCA method causes false alarms and missing data that significantly compromise the reliability of the monitoring systems. In the present article, a new PCA method based on variance sensitive adaptive threshold (T_vsa) is proposed to overcome false alarms which occur in the transient states according to changing process conditions and the missing data problem. The proposed method is implemented and validated experimentally on an electromechanical system. The method is compared with the conventional monitoring methods. Experimental tests and tabulated results confirm the fact that the proposed method is applicable and effective for both the steady-state and transient operations and gives early warning to operators.     

基于PCA-BPNN的膨胀土自由膨胀率预测

为了准确地对膨胀土进行分类,将主成分分析方法（PCA）和BP神经网络方法（BPNN）相结合,进行膨胀土自由膨胀率预测研究。首先,对影响自由膨胀率的几个物性指标数据进行主成分分析,得到主成分数据序列;然后,根据主成分数据序列建立输入样本并应用BP神经网络进行训练,得到PCA-BPNN预测结果,从而预测自由膨胀率。采用南阳膨胀土数据进行实例分析,结果表明,该模型比单一BP神经网络预测方法的效果更好。     

广东省三水市思贤滘水道分流影响因素分析

以数学模型为基础手段,采用恒定总流连续方程、能量方程,分析临界状态下定床恒定流和定床非恒定流状态时思贤滘水道分流影响因素。将特殊推广至一般,找出各水力要素与分流量（比）的关系,并将水位、流量无量纲化,绘制出规律性较强的分流比关系曲线,对思贤滘水道分流的研究作出重大贡献。     

基于主曲线的微阵列数据分类

提出了一种基于主曲线(principal curves)的微阵列数据分类方法(PC)。主曲线是第一主成分的非线性推广,它是数据集合的“骨架”,数据集合是主曲线的“云”。基于主曲线的微阵列数据分类方法,首先利用专门设计的算法在训练数据集上计算出每类样本的主曲线,然后根据测试样本与各类样本主曲线距离的期望方差来确定测试样本所属的类别。实验结果表明,该分类方法在进行小样本微阵列数据分类时性能优于现有的方法。     

面向打印的光谱色彩管理中间空间构造方法

针对光谱色彩管理中光谱空间维度高引起多光谱图像处理时间长、所需存储空间大的问题,提出构造中间空间的方法。首先通过分析色彩管理过程,引入中间空间,建立以中间空间为设备无关颜色空间的光谱色彩管理流程;然后针对多光谱图像的打印输出,采用主成分分析法对打印机特征化光谱样本进行降维,将降维后的特征空间作为中间空间;最后采用特征向量矩阵实现任意多光谱图像数据到中间空间的变换。实验表明,采用打印机特征化光谱样本生成的中间空间与光谱空间的变换效率高,变换的光谱和色度精度高,图像数据降维后能保持源图像光谱的主要信息。     

隐式多项式曲线的信息相似性分析研究

隐式多项式曲线用来描述数据点集合轮廓具有良好的性质,并已经获得广泛的应用。研究一般商业数据的特点,将一维商业数据变换成二维商业数据,使用隐式多项式曲线对其进行拟合描述,计算这些隐式多项式曲线的相似性,进而实现对商业数据的相似性计算分析。实验结果证明了算法的有效性。     

论区间B样条曲线的升阶与割角的关系

为了解决区间B样条曲线的升阶理论问题,提出区间控制多边形概念,利用双次B样条基函数证明了区间B样条曲线具有升阶性质;并阐明了区间B样条曲线的升阶就是对其控制多边形的割角过程.最后证明了当升阶次数趋于无穷时,区间B样条曲线的控制多边形收敛到该曲线.     

Jacobi交上的配对计算

目前已知的配对计算都是在椭圆曲线的平面模型下实现的,比如Weierstrass型曲线、Ed-wards曲线和Jacobi四次曲线。本文第一次讨论空间曲线上配对的具体计算。密码学中所关心的空间曲线主要是三维空间中的二次曲面的交,它与Edwards曲线、Jacobi四次型都有极其紧密的联系,因而研究二次曲面交上的算术与配对将促进我们对Edwards曲线、Jacobi四次曲线上的相关特性的理解。为了讨论的简洁,我们将主要分析Jacobi交,但我们的结果基本上可以类推到其他的二次曲面交上去。我们分析了Jacobi交上的几何特性,构造了Jacobi交上的有效可计算同态,并在此基础上给出了Jacobi交上配对的具体计算。