Fast and accurate image registration using Tsallis entropy and simultaneous perturbation stochastic approximation

The Tsallis measure of mutual information is combined with the simultaneous perturbation stochastic approximation algorithm to register images. It is shown that Tsallis entropy can improve registration accuracy and speed of convergence, compared with Shannon entropy, in the calculation of mutual information. Simulation results show that the new algorithm achieves up to seven times faster convergence and four times more precise registration than using a classic form of entropy.     

图像非广延高斯熵分割的阈值选取方法

图像分割是实现图像目标与背景分离的基础和关键,其中阈值化是最为流行和有效的技术之一。考虑到图像处理作业场景的复杂境况,实时有效的阈值分割方法的提出一直是研究和应用中的极大挑战。针对复杂的工业无损检测图像及红外图像目标分割问题,基于非广延高斯熵提出一种新的图像阈值分割方法。对于图像中像素灰度级不均衡、不规则的随机分布特点,提出方法体现了较好的处理能力。在与实践常用方法的比较实验中,实验结果表明了提出方法的较优异性能和推广应用前景。     

基于Tsallis熵的自适应红外图像边缘检测方法

针对红外图像边缘检测这一难题,结合红外图像的特点,提出了基于Tsallis熵的自适应红外图像边缘检测方法.该方法分别计算图像子空间的边缘与非边缘的Tsallis熵,根据子空间最优Tsallis熵,构造出子空间最佳阈值的评价函数,根据评价函数,选择不同方向的边缘检测模板,增强了图像的边缘信息,从而避免了单一模板造成的边缘丢失现象.实验结果表明,与传统的边缘检测方法相比,该方法对于红外图像可以最大程度上抑制噪声,有效地提高图像的边缘检测效果.     

Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross-entropy

Jaynes's principle of maximum entropy and Kullbacks principle of minimum cross-entropy (minimum directed divergence) are shown to be uniquely correct methods for inductive inference when new information is given in the form of expected values. Previous justifications use intuitive arguments and rely on the properties of entropy and cross-entropy as information measures. The approach here assumes that reasonable methods of inductive inference should lead to consistent results when there are different ways of taking the same information into account (for example, in different coordinate system). This requirement is formalized as four consistency axioms. These are stated in terms of an abstract information operator and make no reference to information measures. It is proved that the principle of maximum entropy is correct in the following sense: maximizing any function but entropy will lead to inconsistency unless that function and entropy have identical maxima. In other words given information in the form of constraints on expected values, there is only one (distribution satisfying the constraints that can be chosen by a procedure that satisfies the consistency axioms; this unique distribution can be obtained by maximizing entropy. This result is established both directly and as a special case (uniform priors) of an analogous result for the principle of minimum cross-entropy. Results are obtained both for continuous probability densities and for discrete distributions.     

基于混沌PSO或分解的二维Tsallis交叉熵阈值分割(英文)

The segmentation effect of Tsallis entropy method is superior to that of Shannon entropy method, and the computation speed of two-dimensional Shannon cross entropy method can be further improved by optimization. The existing two-dimensional Tsallis cross entropy method is not the strict two-dimensional extension. Thus two new methods of image thresholding using two-dimensional Tsallis cross entropy based on either Chaotic Particle Swarm Optimization (CPSO) or decomposition are proposed. The former uses CPSO...     

Application of entropy formulas in detection of denial‐of‐service attacks

The paper compares five entropy formulas (Shannon, Tsallis, Rényi, Bhatia‐Singh, and Ubriaco) and their application in the detection of distributed denial‐of‐service (DDoS) attacks. The Shannon formula has been used extensively for this purpose for more than a decade. The use of the Tsallis and Rényi formulas in this context has also been proposed. Bhatia‐Singh entropy is a novel information metric with promising results in initial applications in this area. Ubriaco proposed an entropy function based on the fractional calculus. In this paper, flow size distribution was used as the input for detection. The type of DDoS attack is SYN flood, and simulation was used to obtain the input dataset. The results show that the Rényi and Bhatia‐Singh detectors perform better than the rest. Rényi and Tsallis performed similarly with respect to the true positive rate, but Rényi had a much lower false positive rate. The Bhatia‐Singh detector had the best true positive rate but a higher false positive rate than Rényi. The Ubriaco detector performed similar to the Shannon detector. With respect to detection delay, Tsallis, Ubriaco, and Shannon produced similar results, with a slight advantage associated with the Ubriaco detector, while Rényi and Bhatia‐Singh had a larger detection delay than the former three.     

Bimodal packet distribution in loss systems using maximum Tsallis entropy principle

A theoretical model of loss system is proposed and analysed within the framework of maximum Tsallis entropy principle. The study provides an explicit expression for state probability distribution of packets in presence of long-range dependent traffic. The unimodal state probability distribution corresponding to well-known Erlang?s loss formula is recovered for Tsallis entropy parameter q = 1. As the parameter q is lowered from unity, it is shown that the state probability distribution makes a transition from unimodal to bimodal. The emergence of bimodality can be regarded as a consequence of long-range dependence. The implication of the model in the design of loss systems is discussed.     

On uniqueness Theorems for Tsallis entropy and Tsallis relative entropy

The uniqueness theorem for Tsallis entropy was presented in H. Suyari, IEEE Trans. Inf. Theory, vol. 50, pp. 1783-1787, Aug. 2004 by introducing the generalized Shannon-Khinchin axiom. In the present correspondence, this result is generalized and simplified as follows: Generalization : The uniqueness theorem for Tsallis relative entropy is shown by means of the generalized Hobson's axiom. Simplification: The uniqueness theorem for Tsallis entropy is shown by means of the generalized Faddeev's axiom     

基于Tsallis交叉熵快速迭代的河流遥感图像分割

为了使河流遥感图像分割的精度和速度进一步提高,本文提出了一种基于二维Tsallis交叉熵快速迭代的河流遥感图像分割方法。鉴于现有的Tsallis交叉熵阈值法运算效率不够高,首先提出了一维Tsallis交叉熵阈值选取的快速迭代算法;然后导出了基于灰度级—邻域平均灰度级直方图的Tsallis交叉熵阈值选取公式,以进一步提高分割精度,并采用递推方式计算阈值选取准则函数中的中间变量,避免其重复运算,加快运算速度;最后,提出了二维Tsallis交叉熵阈值选取的快速迭代算法,推导出相应的公式,大大减少了运算量。大量实验结果表明,与近年来提出的4种阈值分割方法相比,本文方法在对河流遥感图像的分割效果及运行时间上均有明显优势,是河流检测与类型识别系统中可选择的一种快速有效的分割方法。      

蝙蝠优化的二维Tsallis熵多阈值SAR图像分割

针对智能优化SAR图像分割算法存在计算量大、易陷入局部最优、分割精度不够等问题,融合蝙蝠算法和二维Tsallis熵多阈值,提出了一种蝙蝠优化的二维Tsallis熵多阈值SAR图像分割算法。算法利用立方映射均匀化初始蝙蝠种群,引入Levy飞行特征加强算法跳出局部最优能力,使用Powell局部搜索加快算法收敛等3方面改进蝙蝠算法;同时将二维Tsallis熵单阈值分割方法扩展到多阈值分割,建立基于多阈值的选取方法,并结合改进的蝙蝠算法,将二维Tsallis熵多阈值应用于SAR图像分割中。仿真结果表明,与其他智能优化分割算法相比,本分割算法在边缘处理和分割精度上都有明显优势。     

Image fusion metric based on mutual information and Tsallis entropy

A novel image fusion performance metric using mutual information is proposed. The metric is based on Tsallis entropy, which is a one-parameter generalisation of Shannon entropy. Experimental results have confirmed that the proposed metric outperforms the standard MI metric by correlating better with the subjective quality of fused images.     

基于多尺度多方向Gabor变换的Tsallis熵阈值分割方法

为了能在统一框架内处理无模态、单模态、双模态或者多模态直方图情形下的自动阈值选取问题,该文提出一种基于多尺度多方向Gabor变换的Tsallis熵阈值分割方法(MGTE)。该方法先通过Gabor变换得到多尺度乘积图像,然后利用内外轮廓图像从多尺度乘积图像中重构1维直方图,并在重构1维直方图上采用Tsallis熵计算模型来选取4个方向Tsallis熵取最大值时对应的阈值,最后对4个方向的阈值进行加权求和作为最终分割阈值。将提出的方法和5个分割方法在4幅合成图像和40幅真实世界图像上进行了实验。结果表明提出的方法虽然计算效率不占优势,但它的分割适应性和分割精度有明显的提高。     

二维直方图θ-划分Tsallis熵阈值分割算法

鉴于常用二维直方图区域直分法存在错分,最近提出的斜分法不具普遍性,而Tsallis熵与传统的Shannon熵相比,具有普适性且更为有效,本文提出了适用面更广的基于二维直方图θ-划分和最大Tsallis熵的图像阈值分割算法。首先给出了二维直方图θ-划分方法,采用四条平行斜线及一条法线与灰度级轴成θ角的直线划分二维直方图区域,按灰度级和邻域平均灰度级的加权和进行阈值分割,斜分法可视为该方法中θ=45o的特例;然后导出了二维直方图θ-划分最大Tsallis熵阈值选取公式及其快速递推算法;最后给出了θ取不同值时的分割结果及运行时间,θ取较小值时,边界形状准确性较高,θ取较大值时,抗噪性较强,应用时可根据实际图像特点及需求合理选取θ的值。与常规二维直方图直分最大Tsallis熵法相比,本文提出的方法所得分割结果更为准确,抵抗噪声更为稳健,且所需运行时间及存储空间也大为减少。      

Queue length distribution of network packet traffic: Tsallis entropy maximization with fractional moments

A framework based on maximization of Tsallis entropy constrained by fractional moments is proposed to model queue length distribution of number of packets in network traffic exhibiting long-range behavior. For appropriate range of the Tsallis entropy parameter q, it is found that the first moment of number of packets may not exist Based on Euler summation formula, explicit expressions for mean queue length and buffer overflow probability exhibiting power law behavior are obtained. It is shown that in the limiting case as q tends to 1, one recovers the asymptotic results for buffer overflow probability depicting Weibull-like tail.     

Information theoretic framework of trust modeling and evaluation for ad hoc networks

The performance of ad hoc networks depends on cooperation and trust among distributed nodes. To enhance security in ad hoc networks, it is important to evaluate trustworthiness of other nodes without centralized authorities. In this paper, we present an information theoretic framework to quantitatively measure trust and model trust propagation in ad hoc networks. In the proposed framework, trust is a measure of uncertainty with its value represented by entropy. We develop four Axioms that address the basic understanding of trust and the rules for trust propagation. Based on these axioms, we present two trust models: entropy-based model and probability-based model, which satisfy all the axioms. Techniques of trust establishment and trust update are presented to obtain trust values from observation. The proposed trust evaluation method and trust models are employed in ad hoc networks for secure ad hoc routing and malicious node detection. A distributed scheme is designed to acquire, maintain, and update trust records associated with the behaviors of nodes' forwarding packets and the behaviors of making recommendations about other nodes. Simulations show that the proposed trust evaluation system can significantly improve the network throughput as well as effectively detect malicious behaviors in ad hoc networks.     

基于Tsallis熵的红外偏振热像分割算法

传统红外热像存在对比度低、边缘模糊等不足而使目标区域分割困难,红外偏振热像能够凸显边缘和轮廓特征,因此在环境监测、军事侦察、工业无损检测等领域得到广泛的应用,但如何进行红外偏振热像分割目前研究较少。为此,本文提出了一种基于Tsallis熵的红外偏振热像分割算法。首先通过Tsallis阈值对偏振方位角热像进行初分割,然后以最小化初分割热像交集与并集误差率优化Tsallis指数,再利用指数优化后的Tsallis阈值对偏振方位角热像进行优化分割并通过连通域检测去除误分割得到二次分割图,最后以二次分割图交集区域为种子区域、并集区域为边界,通过区域生长法得到最终分割热像。实验结果显示,本文算法相对最小Tsallis交叉熵法、Otsu法和模糊聚类法错分区域小,在主观视觉效果和区域间对比度、形状测度评价指标上有较大的改善,能够更准确地分割出目标。     

Simplification method for textured polygonal meshes based on structural appearance

This paper proposes an image-based simplification method for textured triangle meshes that preserves the structural appearance of textured models. Models used in interactive applications are usually composed of textured polygonal meshes. Since textures play an important role in the final appearance of the simplified model, great distortions can be obtained if texture information is not considered in the simplification process. Our method is based on an information channel created between a sphere of viewpoints and the texture regions. This channel enables us to define both the Shannon entropy and the mutual information associated with each viewpoint, and their respective generalizations based on Harvda–Charvát–Tsallis entropy. Several experiments show that great visual distortions are avoided when textured models are simplified using our method.     

Entropie totale,probabilité relative,maximum de vraisemblance et décision bayesienne subjective. Application au calcul des bornes des probabilités d’erreur de décodage

The main purpose of this paper is to introduce subjectivity in the form of weighting coefficients in decoding problems. Subjectivity is thought of as prior knowledge of meaning. First, by using very simple informational axioms, we derive a model of observation described by the Lorentz equations. This is a new derivation of the model which we already obtained via several different ways. Then, on applying this model to observing the information involved by an event only, we get a concept of relative probability on the one hand, and of generalized relative entropy on the other hand, which we use to calculate the capacity of a channel. Finally, the relative entropy is used to derive a generalized maximum likelihood criterion and a generalized bayesian decision criterion, whereby we can obtain errors bounds for channels when semantics is taken into account, via techniques similar to those of Gallager.     

探讨基于Tsallis熵的视频关键帧提取技术

关键帧是视频中的一组有限数量的帧的子集,一个视频的关键帧序列能够合理地概括该视频信息,从而减少过大的视频数据对生产生活带来的承载负重.本文讨论了基于Tsallis熵的Jensen距离公式——JTD在视频关键帧提取中的应用.根据得到的差异性距离值JTD,首先检查子镜头边界,进而从每个子镜头中抽取一帧作为该镜头的代表帧,最终得到该段视频的关键帧序列.     

图像分割中的交叉熵和模糊散度算法

本文将交叉熵和模糊散度应用于图像分割中,提出了中最优灰度值选取算法,其一是基于均匀分布假设的最小交叉熵算法,其二是利用后难概率的最大类间交叉熵算法,其三是类间最大模糊散度的改进算法,其四是最小模糊散度算法,针对图像阈什化分割的要求,在后两种算法中构造一种新的模糊录改度函数,本文采用均匀测试和开头测试比较各算法的性能,利用多种类型测试 是到的分割结果,显示了所筛算法的有效性和通用性。