模式识别统一熵理论

本文提出的模式识别统一熵理论包括;对模式识别中存在着两种信息源的分析、利用信息熵概念定义模式识别信息熵系统、论述学习和识别过程中信息熵的变化和传递以及提出模式识别信息熵三定理.统一熵理论揭示了模式识别信息过程中特征选择和模式识别性能极限间的内在连系,对模式识别理论研究和系统设计都具有一定指导意义,这一理论在汉字识别的研究和实践中得到了验证.     

Further results on the information theory of deterministic functions and its application to pattern recognition

The author derived recently Shannon entropy and Renyi entropy for deterministic maps (different from the concepts which are utilized by physicists in the study of deterministic chaos) and his purpose herein is to extend the theory and to outline its prospects in pattern recognition. Entropies of random and of distributed functions are defined, and then entropic variance, divergence, mean square divergence and cross-entropic variance are obtained in quite a meaningful way. The results so obtained are used to derive identification criteria for pattern recognition, and an approach involving the local maximization of a multi-model landscape function is suggested. Basically, we are so dealing with an information theory without explicitly referring to probability.     

基于熵特征的雷达辐射源信号识别

针对现有方法识别率低和没有考虑噪声影响的问题, 提出一种新的雷达辐射源信号识别方法. 将近似熵(ApEn)和范数熵(NoEn)构成特征向量, 用神经网络分类器实现自动分类识别. ApEn是定量描述信号复杂性和不规则性的有效测度, NoEn是定量表征信号能量分布的有效参数. 理论分析和实验结果表明, 熵特征类内聚集性强、类间分离度大, 在较大信噪比范围内均能获得非常满意的正确识别率, 证实了所提出方法的有效性.     

融合表面肌电和加速度信号的下肢运动模式识别研究

为了提高下肢运动模式识别率,本文设计了一种融合表面肌电和加速度信号的下肢运动模式识别方法.首先,用局部均值分解将表面肌电信号分解为多个乘积函数（Product Functions,PFs）,再计算PF成分的多尺度排序熵.然后,通过拉普拉斯权重（Laplacian score,LS）特征选择算法选定每路肌电信号的一个尺度排序熵为特征,并把该特征和加速度信号的排序熵组成特征向量.最后,根据类内欧氏距离和类间样本分布,设计了改进的二叉树支持向量机,把特征向量输入该支持向量机进行下肢运动模式分类.实验结果表明所提方法对七个日常动作的平均识别率达到98.62%,相较于其他方法有较高的识别率.     

On Defining Partition Entropy by Inequalities

Partition entropy is the numerical metric of uncertainty within a partition of a finite set, while conditional entropy measures the degree of difficulty in predicting a decision partition when a condition partition is provided. Since two direct methods exist for defining conditional entropy based on its partition entropy, the inequality postulates of monotonicity, which conditional entropy satisfies, are actually additional constraints on its entropy. Thus, in this paper partition entropy is defined as a function of probability distribution, satisfying all the inequalities of not only partition entropy itself but also its conditional counterpart. These inequality postulates formalize the intuitive understandings of uncertainty contained in partitions of finite sets. We study the relationships between these inequalities, and reduce the redundancies among them. According to two different definitions of conditional entropy from its partition entropy, the convenient and unified checking conditions for any partition entropy are presented, respectively. These properties generalize and illuminate the common nature of all partition entropies.     

最大倒数熵/倒数灰度熵多阈值选取

现有的基于Shannon熵的阈值选取方法存在无定义值和零值的缺陷,并且没有考虑目标和背景类内灰度的均匀性。为此,本文针对多目标（背景）图像分割问题,提出了基于最大倒数熵/倒数灰度熵和自适应双粒子群优化（Adaptive Chaotic Variation Particle Swarm Optimization, ACPSO）的多阈值选取方法。首先将最大倒数熵单阈值选取推广到多阈值选取;然后定义了倒数灰度熵,导出了基于最大倒数灰度熵的单阈值和多阈值选取公式;最后给出最大倒数熵/倒数灰度熵多阈值选取的ACPSO算法步骤,实现对多个阈值快速精确地寻优。实验结果表明,与现有的同类方法—基于最大Shannon熵和粒子群优化（Particle Swarms Optimization, PSO）的多阈值选取方法相比,本文提出的方法有明显的优势,已应用于红外弱小目标检测中的阈值分割和卫星云图识别中的数字云图分割,取得了极佳的分割效果。      

面向非合作目标识别的大规模类属超图建模

 针对大量不同成像条件下获得的多视图像,研究利用局部不变特征及其空间布局约束构建用于非合作目标识别的类属超图模型的方法.该方法首先将每一幅图像表示为使用选定的稳健SIFT特征构成的属性图,然后提出了一种属性图相似性传播聚类原理.在给定的F度量的约束下,利用该原理进行聚类,并根据熵函数最小化优化条件,可迭代得到特定目标属性图样本集合的最优聚类,进一步将所获得的聚类简化成以非冗余属性图作为节点的类属超图模型.本文用大量图像样本进行了试验测试.实验结果验证了模型的可扩展性和识别性能.     

基于码重信息熵低码率线性分组码的盲识别

为解决较高误码条件下的低码率二进制线性分组码的盲识别问题,论文提出了基于码重分布信息熵的码长识别方法,而且,它还通过优化传统的矩阵化简方法求解生成矩阵,从而实现对低码率二进制线性分组码的盲识别。理论分析及仿真验证均表明:该方法能实现低码率线性分组码较正确的识别。论文最后进一步对不同码长线性分组码在不同误码率条件下进行了多次仿真,仿真结果验证了该识别方法具有较好的容错性能。     

Probability density function estimation using the MinMax measure

The problem of initial probability assignment which is consistent with the available information about a probabilistic system is called a direct problem. E.T. Jaynes' (1957) maximum entropy principle (MaxEnt) provides a method for solving direct problems when the available information is in the form of moment constraints. On the other hand, given a probability distribution, the problem of finding a set of constraints which makes the given distribution a maximum entropy distribution is called an inverse problem. A method based on the MinMax measure to solve the above inverse problem is presented. The MinMax measure of information, defined by Kapur, Baciu and Kesavan (1995), is a quantitative measure of the information contained in a given set of moment constraints. It is based on both maximum and minimum entropy. Computational issues in the determination of the MinMax measure arising from the complexity in arriving at minimum entropy probability distributions (MinEPD) are discussed. The method to solve inverse problems using the MinMax measure is illustrated by solving the problem of estimating a probability density function of a random variable based on sample data     

基于峭度加权VMD和熵特征的雷达脉内调制识别

针对复杂电磁环境下识别雷达信号脉内调制样式困难以及受噪声影响识别准确率受限的问题,提出了一种将变分模态分解(Variational Mode Decomposition, VMD)与熵特征提取相结合的识别方法。首先,通过基于峭度加权的改进VMD算法对雷达信号进行分解,得到由三个本征模态函数组成的最优分量集合;其次,对各分量分别计算其模糊熵、排列熵和符号熵值,从而实现对熵特征信息提取;最后,将特征向量输入到支持向量机完成识别。相较于其他方法,该方法有着较高的识别准确率和抗噪性能,在2 dB信噪比以上平均识别准确率为94.63%。     

基于信息熵和概率神经网络的海战场目标识别

海战场目标识别是态势评估的基础,采用信息熵进行属性的约简,去除冗余属性,减少无关属性对识别的干扰。利用概率神经网络综合使用RBF神经网络和竞争神经网络进行目标识别。信息熵与概率神经网络结合可以过滤掉传感器信息中的冗余信息,有效地对海战场目标进行识别,并具有一定的泛化能力。通过仿真证明了本方法的有效性。     

分步式短脉冲激光时间分布特征快速识别模型构建

短脉冲激光中时间分布特征会对光束调节产生一定影响,降低其超分辨率,为提高激光测量系统识别精度,构建分步式短脉冲激光时间分布特征快速识别模型。针对脉冲在传输过程中出现畸变的现象,利用傅里叶变换做时频信号转换,得出脉冲传输规律;通过对激光工作物质储能的调节,确保光源质量,采用CCD测量方法获取空间载波条纹图像,从条纹周期中获得脉冲相位分布信息;结合相位分布情况,使用多元线性表达式描述脉冲时间序列;对该序列做Hilbert转化,建立脉冲信号模型,获取脉冲包络特性,重建相位空间,得到时间特征总点数比值,经过对时频熵的计算,识别出时间分布特征,完成完整识别模型的构建。仿真结果表明,设计方法能有效抑制谐波干扰,提高识别精度。     

基于时频图像Zernike 矩特征的欺骗干扰识别

为了有效应对跟踪雷达的三种常见欺骗干扰,提出了一种基于平滑伪魏格纳-维尔分布时频图像的Zernike 矩特征的干扰识别方法。该方法对三种干扰下的雷达接收信号进行时频分析,运用数字图像处理技术对时频图像进行一系列的预处理后,通过Zernike 矩特征提取图像的细节特征组成特征向量进行分类识别。仿真实验证明:该方法有较高的识别率,特别是该方法受信噪比影响较小,能够有效降低噪声对干扰识别的影响,说明了采用图像识别方法对雷达欺骗干扰信号进行分类识别的可行性。通过与其他文献方法的比较,证明了该方法的优越性。     

基于一维距离像的抗箔条干扰算法研究

对箔条进行有效识别是对抗雷达箔条干扰特别是冲淡式干扰的关键。阐述了箔条干扰的原理,分析了舰船和箔条一维距离像的波形稳定性、距离像分布及相邻距离像相关性等差异,提取了距离像峰值点位置熵、距离像分散性以及相邻距离像相关系数三个目标特征,构造了BP神经网络识别器并完成了相应的参数设计以实现目标识别。利用真实数据进行了舰船和箔条的目标识别实验,结果表明该算法识别率高,收敛速度快,便于工程实现,具有较强的应用价值。     

一种基于目标散射鉴别的POLSAR图像地物无监督分类新方法

提出了一种基于目标散射鉴别的POLSAR图像地物无监督分类新方法.该方法首先利用极化散射熵将POLSAR图像地物粗分为高散射随机性、中散射随机性和低散射随机性三种情形;然后提取球面散射、偶次散射和体散射相似性参数将上述三种情形细分为十种;在上述散射分类的基础上,采用新定义的两类目标极化差异度量对地物进行类别迭代调整.由...     

A method for digital processing of bitmap images

A method for processing of graphical information is proposed. The method makes it possible to code contour images with the use of complex numbers unambiguously defined by the image shape. Mapping of a noise bitmap image onto the complex plane is studied. The possibility of solving such recognition problems as object identification and determination of the orientation of a figure in a plane is demonstrated.     

基于K-L交叉熵的岭回归人脸识别

岭回归人脸识别利用正则单形的顶点对每类人脸进行多元标记,通过投影实现高维人脸特征的降维。该算法首先提取人脸图像的局部二进制（LBP）直方图特征向量,通过主成分分析（PCA）和岭回归对该特征向量进行两次降维。识别阶段利用K-L交叉熵计算标记向量和投影后特征向量的相似性,根据熵值最小原则完成对测试样本的类别判断。实验选取ORL和YALE两个标准人脸库对算法进行测试,结果表明,K-L交叉熵测度比传统的欧氏距离测度获得更高的识别率。     

A probability model for simple closed random curves (Corresp.)

A Gibbsian probability measure is defined on the set of possible shapes of simple (i.e., not self-intersecting) and closed curves on a discretized lattice. Expressions have been derived for such quantifies as the entropy, average perimeter length, and area in terms of the model parameters, and an iterative procedure has been devised for generating sample objects according to this distribution.     

Influence of high-resolution processing on background noise distribution in ship-borne radar

When a high-resolution algorithm is applied in a ship-borne radar, its non-linearity and distributional characteristics before high-resolution processing determine the distributional characteristics of background clutter after high resolution and detector design afterwards. Because background noise before high-resolution has physical significance, the statistical model of first-order Bragg lines and second-order components of sea clutter is put forward. Then, by using higher order cumulative quantity's statistical verification of actual measured data, it is concluded that the background noise before high resolution conforms to normal distribution in a ship-borne radar. The non-linearity of the high-resolution algorithm means that the background noise after high-resolution processing conforms to non-normal distribution. Non-normal-distributed clutter mainly includes Weibull, lognormal, and K-clutter. Rayleigh clutter can be seen as a special case of Weibull clutter. These clutter types have different statistical characteristics and can be discriminated by recognition of clutter characteristics. The numerical domain's distribution after high-resolution processing is determined by an improved, minimum entropy, clutter characteristics recognition method based on the AIC rule, namely, a two-parameter domain-scanning method. This identification method has a higher recognition rate. It is verified that the background noise after high-resolution by pre-whitened-constrained-MUSIC conforms to lognormal distribution.     

面向信息隐藏的图像复杂度研究

图像位平面复杂度与人的视觉感受有着密切得关系,研究图像位平面复杂度对图像信息隐藏研究意义重要.本文提出了复杂度度量的新方法,把图像位平面的信息熵作为图像复杂度测度.研究了随机序列游长统计规律,并给出理论推导,利用图像位平面的游程统计规律与随机序列游程统计规律的接近程度,定量地描述了图像复杂度,并从实际应用的角度分析了其合理性,说明了图像位平面复杂度可以作为图像信息隐藏的性能评价指标.