An Effective Steganalysis Algorithm for Histogram-Shifting Based Reversible Data Hiding

To measure the security for hot searched reversible data hiding(RDH)technique,especially for the common-used histogram-shifting based RDH(denoted as HS-RDH),several steganalysis schemes are designed to detect whether some secret data has been hidden in a normal-looking image.However,conventional steganalysis schemes focused on the previous RDH algorithms,i.e.,some early spatial/pixel domain-based histogram-shifting(HS)schemes,which might cause great changes in statistical characteristics and thus be easy to be detected.For recent improved methods,such as some adaptive prediction error(PE)based embedding schemes,those conventional schemes might be invalid,since those adaptive embedding mechanism would effectively reduce the embedding trace and thus increase the difficulty of steganalysis.Therefore,a novel steganalysis method is proposed in this paper to detect recent adaptive RDH schemes and provide a more effective detection tool for RDH.The contributions of this paper could be summarized as follows.(1)By analyzing the characteristics for those adaptive HS-RDH,an effective“flat ground”based detection method is designed to fast identify whether the given image is used to hide secret data;(2)According to the empirical statistical model,double check mechanism is provided to improve the detection accuracy;(3)In addition,to further improve detection ability,some detailed information for secret data,i.e.,its content and embedding location are further estimated.Compared with conventional steganalysis methods,experimental results indicate that our proposed algorithm could achieve a better detection accuracy and meanwhile acquire more detailed information on secret data.

Digital Watermarking Scheme for Securing Textual Database Using Histogram Shifting Model

Information security is one of the most important methods of protecting the confidentiality and privacy of internet users.The greater the volume of data,the more the need to increase the security methods for protecting data from intruders.This task can be challenging for researchers in terms of managing enormous data and maintaining their safety and effectiveness.Protection of digital content is a major issue in maintaining the privacy and secrecy of data.Toward this end,digital watermarking is based on the concept of information security through the insertion and detection of an embedded watermark in an efficient manner.Recent methodologies in the research on digital watermarking result in data distortion after embedding the watermark.This scenario can reduce the efficiency of detecting watermarks as well as violate data confidentiality.This study adapts a robust reversible histogram shifting(HS)technique for textual data in relational databases.Furthermore,the study presents a digital watermarking scheme intended for document copyright protection and proof of ownership.The major advantage of the proposed watermarking scheme is that it can protect digital data and preserve data quality.To the best of our knowledge,this research methodology is promising for use in the HS expansion model for watermarking data on nonnumeric databases.In addition,the results showed that despite exposing the database to an insertion attacks at 50%and 75%of the watermark,the proposed algorithm can recovermore than half of the embedded watermark in all addition and alteration attacks cases.As a result,the watermark information can be retained and restored completely.

A Bi-Histogram Shifting Contrast Enhancement for Color Images

Recent contrast enhancement(CE)methods,with a few exceptions,predominantly focus on enhancing gray-scale images.This paper proposes a bi-histogram shifting contrast enhancement for color images based on the RGB(red,green,and blue)color model.The proposed method selects the two highest bins and two lowest bins from the image histogram,performs an equalized number of bidirectional histogram shifting repetitions on each RGB channel while embedding secret data into marked images.The proposed method simultaneously performs both right histogram shifting(RHS)and left histogram shifting(LHS)in each histogram shifting repetition to embed and split the highest bins while combining the lowest bins with their neighbors to achieve histogram equalization(HE).The least maximum number of histograms shifting repetitions among the three RGB channels is used as the default number of histograms shifting repetitions performed to enhance original images.Compared to an existing contrast enhancement method for color images and evaluated with PSNR,SSIM,RCE,and RMBE quality assessment metrics,the experimental results show that the proposed method's enhanced images are visually and qualitatively superior with a more evenly distributed histogram.The proposed method achieves higher embedding capacities and embedding rates in all images,with an average increase in embedding capacity of 52.1%.

A Modified Stability Analysis of Two-Dimensional Linear Time Invariant Discrete Systems within the Unity-Shifted Unit Circle

This paper proposes a method to ascertain the stability of two dimensional linear time invariant discrete system within the shifted unit circle which is represented by the form of characteristic equation. Further an equivalent single dimensional characteristic equation is formed from the two dimensional characteristic equation then the stability formulation in the left half of Z-plane, where the roots of characteristic equation f(Z) = 0 should lie within the shifted unit circle. The coefficient of the unit shifted characteristic equation is suitably arranged in the form of matrix and the inner determinants are evaluated using proposed Jury’s concept. The proposed stability technique is simple and direct. It reduces the computational cost. An illustrative example shows the applicability of the proposed scheme.

The interaction of nonlinear waves in two-dimensional dust crystals

This paper investigates the collision between two nonlinear waves with different propagation directions in two- dimensional dust crystals. Using the extended Poincare-Lighthill-Kuo perturbation method, two Korteweg-de Vries equations for nonlinear waves in both the ξ and η directions are obtained, respectively, and the analytical phase shifts and trajectories after the collision of two nonlinear waves are derived. Finally, the effects of parameters of the lattice constant a, the arbitrary constant u0η, the forces f（r）, and the colliding angle θ on the phase shifts of both colliding nonlinear waves are examined.

The interaction of nonlinear waves in two-dimensional lattice

This paper investigates the collision between two nonlinear waves with arbitrary angle in two-dimensional nonlinear lattice. By using the extended Poincarge-Lighthill-Kuo perturbation method, it obtains two Korteweg-de Vries equations for nonlinear waves in both the ζ and η directions, respectively, and derives the analytical phase shifts after the collision of two nonlinear waves. Finally, the solution of u（υ） up to O（ε^3） order is given.

基于自适应多特征融合的mean shift目标跟踪

经典mean shift目标跟踪算法简单快速,具有较好的跟踪效果,但是它用单个特征描述目标,易受相似目标与背景的干扰,鲁棒性较差.针对此不足,推导出多特征融合mean shift目标定位公式;为了适应跟踪过程中目标与背景的变化,提出利用概率分布可分性判据动态评价特征对目标与背景的区分能力,并自适应地计算特征融合权重.在上述两个方面的基础上,对mean shift目标跟踪算法进行了改进,提出一种多特征融合mean shift目标跟踪算法.实验结果表明:提出的算法比经典mean shift目标跟踪算法具有更强的抗干扰性能和较高的跟踪精度.

基于空间边缘方向直方图的Mean Shift跟踪算法

传统的基于色彩直方图或空间色彩直方图的Mean Shift跟踪算法,在诸如跟踪目标出现尺度变化的复杂条件下,无法得到准确的跟踪结果。这是因为色彩直方图或空间色彩直方图无法显著区分颜色相近的目标和背景。鉴于此,提出了一种基于空间边缘方向直方图的Mean Shift跟踪算法,使用空间分布和纹理信息作为匹配信息。实验结果表明,该算法能够有效的处理遮挡、光照变化和尺度缩放等复杂情况,对目标进行准确有效的跟踪,改善了传统方法在尺度缩放等方面的局限性。

基于Mean Shift随机游走图像分割算法

针对传统随机游走算法分割目标轮廓易受自然纹理背景干扰,并且算法运行效率低的问题,提出一种基于Mean Shift随机游走图像分割算法.首先应用Mean Shift算法对图像进行预分割,将图像分成许多同质区域,再将其代替经典随机游走算法中节点来建立对应的无向图;将彩色直方图作为区域描述算子,采用欧氏距离与高斯权函数相结合来建立区域间相似性权函数;最后应用离散电势理论计算图中节点间电势值,并根据节点电势值的大小对预分割得到的同质区域进行分类,以实现图像分割.实验结果表明,与传统方法相比,该算法在分割精度和运行效率上都有很大提高.

基于Mean Shift算法的伪装目标跟踪

针对传统Mean Shift跟踪算法对伪装目标进行跟踪时容易陷入局部最大值导致跟偏甚至跟丢的问题,本文首先采用联合直方图来增强对目标特征的描述,然后在跟踪过程中以目标背景区分度为原则,动态更改目标描述模型和通过调整联合直方图各部分的权重来自适应背景的变化,以保证跟踪此类目标的鲁棒性。实验证明,针对与背景相似的伪装目标,改进的Mean Shift算法仍能对其进行有效准确的跟踪。

基于分块颜色直方图的MeanShift跟踪算法

针对基于MeanShift的跟踪算法对目标尺度变化适应能力差并且不能对目标的旋转进行跟踪的缺点,提出了一种基于分块颜色直方图的MeanShift跟踪算法。新算法引入目标旋转和缩放矩阵使得算法能够适应目标的旋转和尺度变化,分块的颜色直方图包含了目标的空间信息,提高了跟踪算法的鲁棒性和适应能力。实验结果表明新算法能够同时对目标的尺度和旋转变化进行稳定的跟踪,改善了原算法的适应能力。

核函数带宽自适应的Mean-Shift跟踪算法

为了实现Mean-Shift跟踪算法中的核函数带宽自适应更新,提出基于比较Bhattacharyya系数的新方法。首先用模板中心加权与目标边缘加权的直方图计算巴氏系数,跟踪时用候选目标边缘加权直方图与模板中心加权直方图计算新的巴氏系数,根据两个系数的大小对核带宽进行10%的缩放。实验表明,该方法有效克服了带宽只能缩小的问题,实现了跟踪窗对目标缩放的自适应性。

改进的抗遮挡MeanShift目标跟踪算法

传统Mean Shift目标跟踪算法通过bin-bin颜色直方图表示目标特征,直方图中往往会混入背景颜色信息,造成跟踪不准确;同时由于Mean Shift算法具有局部最优性,当目标受到严重遮挡丢失后,不能对目标重新定位跟踪。为了解决上述问题,在颜色直方图和抗遮挡能力方面进行了改进。利用交叉bin颜色直方图代替传统的bin-bin颜色直方图表示目标特征,减少背景颜色的干扰,提高Mean Shift算法跟踪精度;当目标受到严重遮挡丢失后,通过一种尺度变化调整机制,在全局范围内搜索目标位置,提高Mean Shift算法抗遮挡能力。实验显示,改进后的算法不仅在背景干扰大时对目标的跟踪精度更高,而且当目标受到严重遮挡丢失后,也能够对目标重新定位跟踪。

基于Mean-shift的改进目标跟踪算法

传统的Mean-shift目标跟踪算法对背景因素比较敏感,采用核加权直方图的方法计算目标模板与候选区域目标特征往往无法实现对运动目标的准确定位。在研究传统算法的基础上,改进了Mean-shift算法中目标特征选取机制,即目标模板采用背景加权,候选目标区域采用核加权。仿真结果表明,该方法实现了在复杂环境背景下对运动目标更加准确的跟踪。

血细胞图像分割的改进MEANSHIFT方法

根据血细胞图像的特点,采用一种基于改进的MEANSHIFT算法,对血细胞图像的G通道直方图和S分量直方图进行处理,自适应地得到白细胞核和其他区域的分割阈值,通过阈值分割将白细胞核从图像中分割出来,再通过后处理得到白细胞浆区域,从而实现白细胞的自适应阈值分割。在对MEANSHIFT算法进行改进后,阈值的获取自适应性更强,实验证明,这种自适应的阈值分割方法快速、有效,并且对于图像颜色变化、染色条件差异等鲁棒性强。

一种特征融合的带宽自适应Mean Shift跟踪算法

传统Mean Shift跟踪算法在目标颜色特征和背景颜色特征相近、尺度变化等情景下效果不理想。提出了一种特征融合且核函数带宽自适应的改进跟踪算法,针对颜色直方图容易受背景区域影响,融合了边缘直方图,并对核函数带宽进行自适应更新。另外,对目标模型的更新进行了相关探索并给出比较结果。结果表明,该算法可以对目标实现更稳定的跟踪,对目标颜色和背景颜色相近、尺度变化等场景具有很好的适应性。

基于Mean-shift的棉花异性纤维图像分割

提出了一种基于均值平移(Mean-shift)算法和膨胀、滤波算法相结合的棉花异性纤维图像分割方法。选择具有代表意义的头发、麻绳和塑料布的灰度图像进行处理,首先对棉花异性纤维图像采用Mean-shift算法进行图像分割,然后对分割后的图像元素进行膨胀处理和滤波处理,实现棉花异性纤维图像的精确分割。实验结果表明,本文采用的图像分割方法具有较强的抗噪声能力,达到了比较满意的分割效果。

基于直方图比的背景加权的Mean Shift目标跟踪算法

针对Mean Shift跟踪算法目标模型中背景像素所造成的目标跟踪定位的偏差,提出了一种基于直方图比的背景加权的目标表示方法。该方法使用由目标核直方图和背景直方图的对数似然比值推导出的隶属度因子作为权值,通过只对目标模型进行加权变换,而不变换目标候选模型的方法,增强了目标和背景的可分性,有效减少了跟踪过程中背景像素的干扰,提高了目标定位的准确性。仿真实验结果表明算法的有效性。

基于Mean shift算法的灰度人脸跟踪

由于灰度图像的信息单一、缺乏描述目标的信息,且易受到光照的影响,导致Meanshift算法在灰度图像跟踪中的应用不稳定,我们采用结合灰度与方向编码特征建立目标模型的策略,提出了在灰度图像中以Meanshift为核心的人脸跟踪算法。实验结果表明,该算法可以成功的克服混乱、遮挡、光照变化以及目标自身的缩放与旋转的影响,并且计算量非常小,对于大小为48像素×68像素的目标,整个算法的计算时间仅为34ms。

面向交通流检测的Mean Shift多目标自适应跟踪算法

随着社会的发展,交通问题越来越严重,交通流检测技术是解决这一问题的重要途径,而目标跟踪又是交通流检测中必不可少的一部分。目前在目标跟踪领域Mean Shift算法是被广泛采用的技术,但是传统的Mean Shift跟踪算法计算量很大,难以实现交通流检测中的多目标实时追踪。鉴于此,提出一种基于线性预测的Mean Shift跟踪算法。该算法引入一个预测矢量,用来预测目标在下一帧可能出现的位置,在跟踪时算法从预测的位置开始迭代,直至收敛于目标真实位置。实验结果表明,该算法从很大程度上提高了原有算法的效率,有利于实时跟踪。而且,为了解决Mean Shift跟踪算法中核函数带宽自适应的问题,还提出了一种基于比较目标中心灰度比例变化来实现带宽自适应更新的新方法。