抵抗几何攻击的数字图像水印技术的研究

数字水印技术是近年来提出的一种有效的数字产品版权保护技术。目前数字水印算法的抗攻击能力还不能完全达到应用的要求，几何变换攻击被认为是提高水印算法鲁棒性的关键问题。本文首先对数字水印攻击，几何变换攻击作用等方面进行了探讨，然后分析了当前图像水印抵抗几何攻击的各种策略，提出了下一步可能的发展方向。     

数字图像几何变换的数据并行方法研究

针对SIMD计算机上的数字图像的几何变换问题,提出了一个新颖的基于阵列平移的数据并行实现方法。在此基础上,给出了数字图像几何变换的数据并行实现算法,并详细分析和讨论了算法的复杂性以及精度要求。     

一种抗几何变换攻击的盲图像水印

几何变换攻击一直是许多数字图像水印的薄弱环节。即使是轻微的旋转、缩放、平移也将使得图像中的水印难以检测出来。本文中，我们提出了一种基于Fourier-Mellin变换的新的盲水印算法，实验证明它对几何变换攻击具有较强的鲁棒性。     

几何变换的误差传播

采用最坏情况法研究几何变换中的误差传播问题．在给出基本几何元素误差域表示方法的基础上，讨论对称、旋转变换的误差传播规律；并利用Minkowski算子，给出计算几何变换后误差域的算法，从而为进一步研究几何误差的控制奠定基础．     

一种高精度的加权鲁棒估计算法

通过构造与余差有关的目标函数,给出了一种高精度估计图像几何变换矩阵的鲁棒算法——加权鲁棒估计算法。算法首先对原始数据进行计算得出图像间几何变换关系矩阵,再利用特征点周围的灰度信息进行权值计算。在拟合几何变换矩阵的迭代过程中,寻找目标函数最小的匹配关系,从而对两幅图像进行精确配准。实验结果证明,使用该技术后算法的图像配准效果较理想,鲁棒性好,真实感强,实际应用价值较高。     

基于特征点的抗几何变换图像被动认证算法

针对同幅图像的区域复制篡改问题,提出一种基于SIFT特征点的抗几何变换数字图像被动认证算法。在利用SIFT算法提取出图像中的SIFT特征点后,对特征点进行匹配。根据同一幅自然图像不会存在互相匹配特征点的这一特性,可以检测出篡改图像中平移、旋转、缩放等几何变换的区域。实验结果证明,该算法能够对抗区域复制篡改的几何变换。     

基于HSV颜色空间的彩色图像的盲水印算法研究

提出了一种新的针对彩色图像的盲水印算法。算法通过在彩色图像空域中嵌入形状和排布都比较规则的锚点来标记图像中嵌入水印的位置,从而在图像经过缩放裁剪等几何变换后仍然能够比较容易地找到水印嵌入的准确位置。实验表明算法对色彩和几何变换都有良好的鲁棒性。     

改进区域求和表算法

分析了一般的纹理映射反走样算法,提出了对纹理映射几何变换过程简化的改进区域求和表算法。算法通过对几何变换的局部线性化使得映射后象素多边形为平行四边形,然后在区域求和表法的基础上对矩形包围盒进行矩形自适应分割来完成反走样处理,实验表明改进算法在计算量增加了不大的基础上提出了图形质量。     

基于包围盒编码的三维线段裁剪新算法

提出一种新型包围盒，该包围盒由12个45。面组成，且包容原裁剪窗体，则落在包围盒外的线段必然在裁剪窗体之外；同时引入三维到二维投影，进行二次编码舍弃窗外线段；最后通过基于包围盒编码分区的几何变换完成裁剪过程．常规包围盒一次编码、新型包围盒二次编码、基于编码分区的几何变换求交这三个步骤构成了基于包围盒编码的三维线段裁剪新算法，实验结果表明，文中算法提高了裁剪效率并具有很好的稳定性。     

基于复数小波域的图形水印方法

提出了一种基于复数小波域的2维矢量图形的水印算法，用于2维矢量图形的版权保护。该算法利用了Dual Tree复数小波变换的优点，将水印嵌人相对坐标线(relative coordinates line)的复数小波域中。经初步实验结果验证，该算法对于通常的图形几何变换，如平移、旋转、缩放以及局部修改攻击，均有令人满意的鲁棒性。     

基于局部冲突分配策略的DST算法研究

为有效克服证据理论(DST)组合规则存在的小足,提出一种基于局部冲突分配策略的DST改进算法.首先根据证据距离获得各个证据的相互支持度,并将支持度归一化为证据的相对可信度;然后通过分析局部冲突产生的原因,将冲突信息只分配给产生冲突的焦元,且分配给焦元的信息取决于证据之间焦元的信任度和相似度.实验结果表明,所提出的算法提高了证据合成结果的可靠性与合理性,可有效地解决高冲突证据融合的问题.     

多源冲突证据的智能融合算法研究

与Dempster-Shafter理论（DST）相比,Dezert-Smarandache理论（DSmT）通过保留证据冲突项作为数据融合的焦元,从而可以很好地解决在证据发生高冲突情况下的信息融合问题。但是因为DSmT算法增加了矛盾焦元,致使推理过程中的计算量加大,更容易产生焦元爆炸的问题。针对上述问题,提出一种结合两者优点的DST-DSmT智能算法。该算法以证据之间的冲突质量作为判断依据,当冲突质量较小时采用DST算法,反之则采用DSmT算法,以期在保证融合效果的情况下,减小计算量。以P2-DX机器人为实验平台,以具体算例验证了方法的正确性和有效性。     

New primal–dual algorithms for Steiner tree problems

We present new primal–dual algorithms for several network design problems. The problems considered are the generalized Steiner tree problem (GST), the directed Steiner tree problem (DST), and the set cover problem (SC) which is a subcase of DST. All our problems are NP-hard; so we are interested in their approximation algorithms. First, we give an algorithm for DST which is based on the traditional approach of designing primal–dual approximation algorithms. We show that the approximation factor of the algorithm is k, where k is the number of terminals, in the case when the problem is restricted to quasi-bipartite graphs. We also give pathologically bad examples for the algorithm performance. To overcome the problems exposed by the bad examples, we design a new framework for primal–dual algorithms which can be applied to all of our problems. The main feature of the new approach is that, unlike the traditional primal–dual algorithms, it keeps the dual solution in the interior of the dual feasible region. The new approach allows us to avoid including too many arcs in the solution, and thus achieves a smaller-cost solution. Our computational results show that the interior-point version of the primal–dual most of the time performs better than the original primal–dual method.     

用于区域表达的线性数字搜索树编码

本文提出一种用于区域表达的数据结构——数字搜索树(DST)及其线性化编码(LDST)。给出了在正方形区域图象最坏情况下的数据压缩公式,公式表明在图象分辨率较高时用LDST可使数据得到有效的压缩。最后,本文还给出了LDST与线性四叉树之间的转换算法及时间复杂度分析。     

Mapping the health of mature deciduous forest stands by fusing multisource geospatial data with Dempster's combination rule

In Canada, forest companies and government are faced with the important challenge of monitoring forest stand health. This task is especially difficult when the objective is to monitor the health of mature deciduous stands. Mapping methods for tree health have been proposed using multispectral and hyperspectral airborne sensors; however, acquiring airborne data over large areas remains costly. In addition, some studies have pointed out that forest dieback is characterized by multi-causality. Therefore, we propose a large-scale mapping method which includes a model to parameterize several factors influencing forest vigour. A high-spatial resolution satellite image was fused with a series of biophysical parameters using the Dempster–Shafer theory (DST). The study was performed over mature deciduous forests in the province of Québec, Canada. The fusion of a Satellite Pour l’Observation de la Terre (SPOT)-5 high resolution geometric (HRG) image with a surface deposit map and an ice storm damage-intensity map provided the best results, improving the overall accuracy by 15.1%, when compared with a K‐nearest-neighbour (KNN) algorithm using the SPOT-5 image alone. Moreover, the DST improved the accuracy of the vigour class identification, halving the standard deviation when compared with the KNN method. This study shows how the DST can be used to model the influence of biophysical parameters when combined with multispectral information to better assess the health of mature deciduous stands.     

无线传感网中分布式信号检测的多维特征值算法优化研究

在大规模无线传感网的分布式信号检测中,针对相关性较高并有一定冗余度的数据集,在保证数据采集可信任的情况下,通过高效算法提高精度是重要的研究方向。 提出一种分散功率算法DPM,用于分布式计算样本协方差矩阵的最大特征值,通过将平均共识和迭代功率法相结合,在相对少量样本和有限次数迭代的条件下,实现了协方差矩阵最大特征值的较快收敛速度和较高精度估计。对比MECD算法和DST算法,仿真结果表明,新算法有效减少了信号样本数和迭代次数,收敛速度较快,可获得更高的检测精度。     

Evolvable dialogue state tracking for statistical dialogue management

Statistical dialogue management is the core of cognitive spoken dialogue systems (SDS) and has attracted great research interest. In recent years, SDS with the ability of evolution is of particular interest and becomes the cuttingedge of SDS research. Dialogue state tracking (DST) is a process to estimate the distribution of the dialogue states at each dialogue turn, given the previous interaction history. It plays an important role in statistical dialogue management. To provide a common testbed for advancing the research of DST, international DST challenges (DSTC) have been organised and well-attended by major SDS groups in the world. This paper reviews recent progresses on rule-based and statistical approaches during the challenges. In particular, this paper is focused on evolvable DST approaches for dialogue domain extension. The two primary aspects for evolution, semantic parsing and tracker, are discussed. Semantic enhancement and a DST framework which bridges rule-based and statistical models are introduced in detail. By effectively incorporating prior knowledge of dialogue state transition and the ability of being data-driven, the new framework supports reliable domain extension with little data and can continuously improve with more data available. Thismakes it excellent candidate for DST evolution. Experiments show that the evolvable DST approaches can achieve the state-of-the-art performance and outperform all previously submitted trackers in the third DSTC.     

Implementation of a declarative state-transition system

A declarative programming style is claimed to have significant advantages from a software engineering point of view. However, these benefits cannot generally be realized when writing programs that are concerned with changing state, such as environments and programming tools. Declarative state-transition (DST) systems have been proposed as a solution to this problem. In DST systems, computation and update are separated. Programs are interpreted as defining functions or relations over states, and update follows successful computation of new states. Support for persistent state and atomic, serializable transactions facilitates the implementation of programming environments and tools. This paper describes an implementation scheme for DST systems. The scheme is illustrated by a presentation of the implementation of PPS, a DST system for parallel logic programming.     

Blind separation of audio signals using trigonometric transforms and wavelet denoising

This paper deals with the problem of blind separation of audio signals from noisy mixtures. It proposes the application of a blind separation algorithm on the discrete cosine transform (DCT) or the discrete sine transform (DST) of the mixed signals, instead of performing the separation on the mixtures in the time domain. Wavelet denoising of the noisy mixtures is recommended in this paper as a preprocessing step for noise reduction. Both the DCT and the DST have an energy compaction property, which concentrates most of the signal energy in a few coefficients in the transform domain, leaving most of the transform domain coefficients close to zero. As a result, the separation is performed on a few coefficients in the transform domain. Another advantage of signal separation in transform domains is that the effect of noise on the signals in the transform domains is smaller than that in the time domain due to the averaging effect of the transform equations, especially when the separation algorithm is preceded by a wavelet denoising step. The simulation results confirm the superiority of transform domain separation to time domain separation and the importance of the wavelet denoising step.     

Applications of Belief Functions in Business Decisions: A Review

In this paper, we review recent applications of the Dempster-Shafer theory (DST) of belief functions to auditing and business decision-making. We show how DST can better map uncertainties in application domains than Bayesian theory of probabilities. We review the applications in auditing around three practical problems that challenge the effective application of DST, namely, hierarchical evidence, versatile evidence, and statistical evidence. We review the applications in other business decisions in two loose categories: judgment under ambiguity and business model combination. Finally, we show how the theory of linear belief functions, a new extension to DST, can provide an alternative solution to a wide range of business problems.