一种缩放情况下区分原始区域和篡改的方法

为了在检测结果中区分出原始区域和粘贴区域,提出一种基于尺度不变特征转换(SIFT)和重采样痕迹的图像盲检测算法。该算法首先将图像分成互不重叠块,利用SIFT算法找出每个块中的关键点,对关键点匹配;然后在每个块中以匹配关键点的中心为种子进行区域生长得到篡改区域;最后分析每个图像块的频谱图,计算出缩放因子,区分原始区域和复制区域。实验结果表明,该算法简便快捷,在能够对篡改区域进行准确定位的同时,并能区分原始区域和复制区域。     

利用二次采样技术进行扫频测量非线性抑制

基于溅散板型抛物面天线产生的已知参考信号，介绍了一种通过对FM／CW近程雷达回波中频信号进行二次采样抑制VCO扫频非线性的技术。给出了天线设计特点以及这种方法的基本原理和注意事项。此外，在暗室内进行的模拟实验表明，这种方法设计合理，可行有效，大大抑制了扫频非线性引起的一维成像频谱展宽和偏移现象。     

基于LabVIEW平台的重采样技术的软件实现

介绍了基于LabVIEW虚拟平台的重采样技术的实现，该方法是将三次样条插值应用到重采样技术当中，与纯粹的样本点抽取相结合，实现对高采样率的数据进行任意比例采样率的重采样。经过实验验证，该方法可以很好地满足仿真和动态数字信号分析的需要。     

非标定图像对最小化畸变几何校正方法研究

图像校正需要对图像进行透视变换,不可避免会产生欠采样或过采样问题,同时射影变换会引起图像扭曲,造成图像对之间相似性丢失.为了降低图像校正后的畸变程度,重点研究了图像校正过程中不同类型图像畸变问题.通过图像对规范化简化基础矩阵,并通过考虑几何约束简化校正变换矩阵;然后求取射影变换权值变化测度之和的极值以实现最小化射影畸变;最后采用Jacobian行列式度量校正前后的局部区域变化,求得最小化重采样效果的矩阵系数.采用该校正变换矩阵对非标定图像对进行平面校正可以减轻射影扭曲程度和重采样效果.     

Edge-preserving texture filtering for real-time rendering

Texture filtering is essential in enhancing the visual quality of real-time rendering. Conventional schemes do not consider the characteristics of texture content, thus the sharpness of edges in texture images cannot be retained. This paper proposes a novel texture-filtering algorithm, which consists of edge-preserving interpolation and edge-preserving MIP-map prefiltering. The memory bandwidth requirement is kept the same as in conventional schemes by dynamically adjusting the interpolation kernel. Hardware implementation is also provided to show the real-time processing capability. Published online: 28 January 2003     

Sufficient bootstrapping

In this paper, we introduce an idea we refer to as sufficient bootstrapping, which is based on retaining only distinct individual responses, and also develop a theoretical framework for the techniques. We demonstrate through numerical illustrations that the proposed sufficient bootstrapping may be better than the conventional bootstrapping in certain situations. The expected gain by the sufficient bootstrapping has been computed for small and large sample sizes. The relative efficiency shows that there could be significant gain by the sufficient bootstrapping and it could reduce computational burden. Variance expressions for both the conventional and sufficient bootstrapping sample means are derived. Here the word “sufficient” is being used in the sense that it is “sufficient to take just one of any duplicated items in the bootstrap sample” and is not tightly connected to sufficiency in terms of any likelihood perspective. R code for comparing bootstrapping and sufficient bootstrapping are provided. A huge scope of further studies is suggested.     

基于实例重要性的SVM解不平衡数据分类

在不平衡数据分类问题中,作为目标对象的少数类往往不易识别.常见方法存在需要显式设置实例重要度、仅仅间接支持少数类的识别等缺点.由此,文中提出基于实例重要性的支持向量机--ⅡSVM.它分为3个阶段.前两个阶段分别采用单类支持向昔机和二元支持向量机,将数据按照"最重要"、"较重要",和"不重要"3个档次重新组织.阶段3首先选择最重要的数据训练初始分类器,并通过显式设置早停止条件,直接支持少数类的识别.实验表明,ⅡSVM的平均分类性能优于目前的主流方法.     

Can human disturbance promote nestedness? Songbirds and noise in urban parks as a case study

Several explanations have been proposed to understand the existence of nested subset patterns in biological communities, including selective extinction, differential colonization, nested species-habitat relationships, random placement from a common species pool, and human disturbance. We study if songbird assemblages inhabiting urban parks in the Puebla-Cholula Metropolitan Area (Mexico, an urbanizing country) exhibit a nested subset pattern, and apply information-theoretic (IT) methods to rank the likelihood of the abovementioned processes as competing hypotheses explaining nestedness. A total of 21 study sites were considered, where 38 Passeriformes species were retained for nestedness analyses (13 species with preferences for forest habitats, and 25 for non-forest habitats). Neither the number of species per site nor the number of sites per species followed expectations from the random placement hypothesis. In addition, using the NODF index for measuring nestedness and different null models for generating random matrices, we found that songbird assemblages inhabiting urban parks were significantly more nested than expected. The IT approaches we followed evidenced that the two most important ecological processes promoting nestedness were selective extinction and human disturbance: site richness orderly increased according to area, and decreased according to background noise levels. Results are discussed taking into account habitat-related sound degradation and transmission properties, and differential susceptibility to anthropogenic noise among songbirds with preferences for forest versus non-forest habitats. To the best of our knowledge, this is the first time that noise pollution, a pervasive kind of human disturbance in modern cities, is acknowledged as a factor promoting nestedness in bird communities.     

Editor's Report

Bootstrap resampling methods have emerged as powerful tools for constructing inferential procedures in modern statistical data analysis. Although these methods depend on the availability of fast, inexpensive computing, they offer the potential for highly accurate methods of inference. Moreover, they can even eliminate the need to impose a convenient statistical model that does not have a strong scientific basis. In this article, we review some bootstrap methods, emphasizing applications through illustrations with some real data. Special attention is given to regression, problems with dependent data, and choosing tuning parameters for optimal performance.     

Stability-based validation of bicluster solutions

Bicluster analysis is an unsupervised learning method to detect homogeneous or uniquely characterized two-way subsets of objects and attributes from a data set. It is useful in finding groups that may not be found by the traditional cluster analysis and in interpreting the groups intuitively, especially for high-dimensional data sets. Because of these advantages, over the last few years, various biclustering algorithms have been developed and applied to bioinformatics and text mining area. However, research into validation of bicluster solutions is rare. We propose a new procedure of validating bicluster solutions by developing a stability index to measure the reproducibility of the solution under variation in the input data set. By generating random resample data sets from the input data set, obtaining bicluster solutions from them, and evaluating the expected agreement of the solutions to the bicluster solution for the original input data set, we quantify the stability of the bicluster solution. Experiments using three artificial data sets and two real gene expression data sets indicate that the proposed method is suitable to validate bicluster solutions.