基于奇异值分解的图像匹配方法

传统的图像匹配方法中, 由于实时图和参考图之间存在着灰度差异和几何形变,仅用灰度作为特征进行匹配算法的性能很容易受到影响。文中提出了一种基于奇异值分解的图像匹配方法。该方法首先利用奇异值分解方法,求出模板图像矩阵的奇异值及奇异值向量,用它们作为模板图像的特征代替传统算法中的灰度对两幅待匹配图像进行全局搜索定位。由于奇异分解方法所特有的优越性,匹配实验取得了良好效果。实验结果验证了该方法的有效性。     

块奇异值分解和量化实现的图像数字水印算法

讨论了当前将水印嵌入在块奇异值分解（SVD,singular value decomposition）后最大奇异值中的图像数字水印算法不足,进而提出了两种新的将水印嵌入在块SVD后最大奇异值除外的其余奇异值中的图像数字水印算法。两种新算法均采用了量化嵌入策略,从而使两种算法在提取水印时无需任何原始信息的帮助。实验结果表...     

基于奇异值分解的特征跟踪方法

在传统的基于模板匹配的跟踪方法中，均是给定一个模板，然后从图像中各个位置取出一个个与模板大小一致的区域进行相似性度量，找出与模板距离最小的一个区域作为当前模板，以便进行下一步的匹配跟踪工作。在景象匹配和相关跟踪过程中，由于所面临的大多数是变化的场景，实时获取的图像与预存模板之间存在比较大的差异，传统相关匹配方法的应用就会受到限制；而且在跟踪过程中，随时更新模板会造成跟踪性能对扰动过分敏感，从而产生漂移。首先拍摄目标不同角度的图像（尽可能包含目标可能出现的所有情况），构成目标图像训练集合，抽取出特征矩阵，对它进行奇异值分解，构成一个关于目标的多维空间。然后再用匹配方法在全局范围搜索，找出目标的大致位置，并利用收敛方法在确定的大致位置内进行搜索，确定目标的仿射变换系数，从而得到一个目标位置的确切描述。     

基于奇异值分解的自适应模板更新研究

研究了基于奇异值分解的图像匹配和目标跟踪问题。由于图像的奇异值特征具有良好的稳定性,可以将奇异值当作一种有效的代数特征来描述并表征图像。根据所定义的奇异值缩放不变量提出了一种基于奇异值分解的模板更新算法。在算法中,根据奇异值向量的缩放不变特征来度量当前模板内的目标信息,然后根据所定义的置信度自动计算更新后所需的模板大小,从而使更新后的模板更有效地包含目标。试验表明：提出的模板更新算法在序列图像的目标跟踪中具有较好的实用性。     

A robust audio watermarking scheme based on reduced singular value decomposition and distortion removal

This paper presents a blind audio watermarking algorithm based on the reduced singular value decomposition (RSVD). A new observation on one of the resulting unitary matrices is uncovered. The proposed scheme manipulates coefficients based on this observation in order to embed watermark bits. To preserve audio fidelity a threshold-based distortion control technique is applied and this is further supplemented by distortion suppression utilizing psychoacoustic principles. Test results on real music signals show that this watermarking scheme is in the range of imperceptibility for human hearing, is accurate and also robust against MP3 compression at various bit rates as well as other selected attacks. The data payload is comparatively high compared to existing audio watermarking schemes.     

Fragile watermarking scheme for tamper localization in images using logistic map and singular value decomposition

This paper presents a fragile watermarking scheme for tamper localization using Singular Value Decomposition (SVD) and logistic map. The proposed scheme divides the image into blocks of size 2 × 2 pixels and generates an 8-bit watermark from each block. The watermark is computed by permuting the six Most Significant Bits (MSBs) of each pixel in the block using the logistic map, followed by SVD. To secure, the watermark thus generated is further encrypted using the logistic map. This encrypted watermark is embedded into 2 Least Significant Bits (LSBs) of each pixel to enable tamper detection and localization. The experimental results demonstrate that the proposed scheme can precisely locate tampered regions under copy-paste, content removal, text addition, noise addition, vector quantization, collage, content only, and constant feature attacks. Tamper localization accuracy is better or comparable to the state-of-the-art tamper localization algorithms.     

Security analyses of the watermarking scheme based on redundant discrete wavelet transform and singular value decomposition

This study analyzes the recent image watermarking schemes based on redundant discrete wavelet transform (RDWT) and singular value decomposition (SVD), and shows that in fact they are insecure and cannot be used for protecting the rightful ownership. The RDWT-SVD watermarking directly embeds a grayscale watermark image of the same size with the host image into the singular value matrix of the RDWT-transformed host image, then produces the left and right orthogonal matrices as side information which is later used in the watermark extraction stage. The RDWT-SVD approach enjoys the advantage of the RDWT redundancy to achieve a high embedding capacity, and preserves the watermark imperceptibility by exploiting the SVD stability properties. It is claimed that RDWT-SVD watermarking is robust against several common image processing and geometrical attacks, yet a fundamental flaw in the RDWT-SVD scheme is found, which leads to severe the false positive issue. Three vulnerable attacks should be considered in the RDWT-SVD scheme: (1) An attacker can easily claim the owner watermarked image; (2) the owner has the ambiguity because of the wrong side information usage, and (3) the owner can extract the correct watermark from arbitrary image. Thus, it is important to highlight these attacks when implementing the RDWT-SVD watermarking scheme.     

No-reference image blur index based on singular value curve

We describe a new no-reference blur index for still images based on a singular value curve (SVC). The algorithm is composed of two steps. First, the singular value decomposition is performed on the image to be blur-assessed. Then an image blur index is constructed from the singular value curve. Experimental results obtained on four simulated blur databases and on the Real Blur Image Database show that the proposed SVC algorithm achieves high correlation against human judgments when assessing the blur distortion of images.     

基于奇异值分解的图像目标跟踪算法

传统相关跟踪方法是利用模板图像与目标图像对应像素的灰度差异信息进行跟踪,它对旋转变化敏感,且存在跟踪累积误差,容易导致模板漂移而丢失目标。文中提出基于奇异值分解的跟踪算法,算法首先建立模板图像训练集合,利用奇异值分解方法,张成模板图像特征空间,然后求出模板图像在特征空间里的投影值,代替传统算法中灰度对两幅待匹配图像进行的全局搜索定位。在进行投影值间的相似性度量时,欧氏距离同等对待所有的特征向量不移合理,文中采用了一种鲁棒估计方法,可以对不同距离的值做不同处理。匹配跟踪实验效果良好。     

Saliency detection based on singular value decomposition

Saliency detection has gained popularity in many applications, and many different approaches have been proposed. In this paper, we propose a new approach based on singular value decomposition (SVD) for saliency detection. Our algorithm considers both the human-perception mechanism and the relationship between the singular values of an image decomposed by SVD and its salient regions. The key concept of our proposed algorithms is based on the fact that salient regions are the important parts of an image. The singular values of an image are divided into three groups: large, intermediate, and small singular values. We propose the hypotheses that the large singular values mainly contain information about the non-salient background and slight information about the salient regions, while the intermediate singular values contain most or even all of the saliency information. The small singular values contain little or even none of the saliency information. These hypotheses are validated by experiments. By regularization based on the average information, regularization using the leading largest singular values or regularization based on machine learning, the salient regions will become more conspicuous. In our proposed approach, learning-based methods are proposed to improve the accuracy of detecting salient regions in images. Gaussian filters are also employed to enhance the saliency information. Experimental results prove that our methods based on SVD achieve superior performance compared to other state-of-the-art methods for human-eye fixations, as well as salient-object detection, in terms of the area under the receiver operating characteristic (ROC) curve (AUC) score, the linear correlation coefficient (CC) score, the normalized scan-path saliency (NSS) score, the F-measure score, and visual quality.     

基于多分辨奇异值分解的多聚焦图像融合

提出了一种多分辨奇异值分解（MSVD）的新框架,并把它应用于多聚焦图像融合中.首先,基于分块算法原理,利用奇异值分解获得具有不同分辨率的一幅近似和三幅细节图像.然后结合重构算法,给出了图像的融合框架.其次,对比基于离散小波变换（DWT）的融合算法,基于MSVD的融合效果更好,而且 MSVD的基向量只依赖于图像本身而不像小波需要固定的基.最后,采用客观性能指标对结果图像进行评价.实验结果表明,本文的方法不仅简单易行,而且图像表现出良好的视觉效果,清晰度和空间频率都有很大提高.     

超分辨测向中奇异值分解的FPGA实现

奇异值分解是超分辨测向技术的核心组成部分,现有的并行实现方案适用范围窄,运算量大,迭代时间长.为了满足测向接收机系统的高实时性需求,结合双边Jacobi算法的交换策略和单边Jacobi算法的求角结构,提出了一种改进的实现方法.该实现方法修正了脉动阵列的收敛性问题,提高了复数矩阵的收敛速度.同时,给出了算法的现场可编程门阵列(FPGA)实现结构.仿真结果证明该方案耗时在百微秒以内,能够应用于电子侦察设备.     

图像奇异值特征矢量缩放不变性分析及应用

图像奇异值特征矢量以其稳定性和它在转置、平移、旋转和镜像变换下具有的不变性,被认为是一种图像的代数特征,广泛应用于图像匹配和图像识别。通过矩阵运算,证明了奇异值特征矢量在图像做缩放变换时具有不变性,并将这种性质运用于图像匹配。实验结果表明这种不变性与奇异值特征矢量在图像做转置、平移、旋转和镜像变换时所具有的不变性一样,是奇异值特征矢量能够作为图像代数特征的一个重要依据。     

Blind image quality assessment using a reciprocal singular value curve

The reciprocal singular value curves of natural images resemble inverse power functions. The bending degree of the reciprocal singular value curve varies with distortion type and severity. We describe two new general blind image quality assessment (IQA) indices that respectively use the area and curvature of image reciprocal singular value curves. These two methods almost require very little prior knowledge of any image or distortion nor any process of training, and they can handle multiple unknown distortions, hence they are no-training methods. Experimental results on five simulated databases show that the proposed algorithms deliver quality predictions that have high correlation with human subjective judgments, and that are competitive with other blind IQA models.     

基于高维张量奇异值分解的图像加密

现有基于奇异值分解(SVD)的彩色信息加密系统提供了一种光学矩阵分解方案、安全的密文和敏感的密钥。高维张量奇异值分解(HOSVD)是SVD矩阵的自然线性延伸,提出了一种基于HOSVD的彩色图像加密算法。在加密过程中,HOSVD比SVD提供了更多的密文乘法组合次序。这些乘法组合次序可以有效地增加未经授权的解密难度。在解密过程中,HOSVD的重建精度比SVD更高。这些优点提高了准确性、安全性和鲁棒性。通过对100个图像测试数据集的计算机仿真验证了该算法的可行性。     

基于奇异值分解的PCNN红外与可见光图像融合

为了提升红外与可见光图像融合精度,提出了一种基于局部区域奇异值分解的自适应PCNN红外与可见光图像融合算法。利用局部区域奇异值构造局部结构信息因子,作为PCNN对应神经元的链接强度。经过PCNN点火处理,获得源图像的点火映射图,通过比较选择算子,选择源图像中明显特征部分生成融合图像。采用多组红外与可见光图像进行融合实验,并对融合结果进行客观评价。实验结果表明本文提出的算法在主观和客观评价上均优于已有文献的一些典型融合算法,可获得更好的融合效果。     

基于SVD的小波变换图像去噪方法

针对传统SVD图像去噪方法的不足,提出了一种基于SVD分解的小波分解图像去噪方法。通过对小波变换的系数矩阵进行奇异值分解,将其中的信号特征成分和噪声分解到不同的正交子空间中,在子空间中选取集成信号特征成分的奇异值矢量进行重构,从而提取出淹没在噪声中的信号成分。实验结果表明该文提出的方法适用于图像信号的提取,与传统的SVD去噪方法相比,它提取出的信号特征成分更完整,信噪比更高。     

Security analysis of weighted network anonymity based on singular value decomposition

The security of anonymous method based on singular value decomposition (SVD) in the privacy preserving of weighted social network was analyzed.The reconstruction method in network with integer weights and the inexact reconstruction method in network with arbitrary weighted were proposed.The $\frac{\epsilon }{N}$ -tolerance was definited to measure its safety.It was also pointed out that the upper bound of ε (the reconfigurable coefficient) obtained in current spectral theories was so conservative that lacks of guidance.The reconfigurable coefficients of random networks,Barabasi-Albert networks,small world networks and real networks were calculated by experiment.Moreover,the reconfigurable coefficients of double perturbation strategies based on SVD were also tested.Experimental results show that weighted social networks have different tolerances on spectrum loss,and there is a close relationship between its tolerance and network parameters.     

基于Pignistic概率转换和奇异值分解的证据冲突度量方法

针对常见证据冲突度量方法适应性差、准确性低的问题,提出了一种基于Pignistic概率转换和奇异值分解的证据冲突度量方法。首先通过Pignistic概率转换将证据焦元差异映射到信度差异上,构建证据复合信任函数矩阵。然后采用奇异值分解的方法提取矩阵特征,根据奇异值特性将矩阵特征空间划分为相似子空间和冲突子空间,综合考虑证据矩阵相似特性和冲突特性,将冲突子空间奇异值与相似子空间奇异值之比作为新的冲突度量因子。最后在全冲突场景、变信度场景、变焦元场景、焦元嵌套场景等多种证据冲突场景下将所提方法与常见方法进行了对比分析,结果表明所提方法具有适应性广、准确性高、稳定性好的特点。