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。     

基于奇异值分解的图像去噪

提出了利用奇异值分解去除图像噪声的方法。从矩阵的角度出发,通过对图像矩阵进行奇异值分解,将包含图像信息的矩阵分解到一系列奇异值和奇异值矢量对应的子空间中,然后通过有效奇异值重构图像矩阵达到去噪目的。试验利用MATLAB通过对MRI(核磁共振)医学图像进行去噪处理,验证了奇异值分解的去噪效果,并且通过对多幅图像的试验结果进行分析,得到了去噪重构图像时所需有效奇异值数目的统计值。     

基于奇异值分解的光电跟踪系统标定方法

针对地面光电跟踪系统的误差定位进行了理论分析,确定影响其目标定位精度的误差因素主要是垂直度误差、零位误差、基座安装误差等,给出了光电跟踪系统的标定流程。利用标定过程中坐标变换的顺序特性,通过奇异值分解原理给出了坐标变换的变换矩阵,包括平移矩阵、旋转矩阵及其欧拉角;同时通过标定流程的迭代,确定了固定俯仰偏移角度的大小。最后通过试验验证了此标定流程的准确性。     

基于奇异值分解动态寻找最佳压缩比的方法

针对奇异值分解变换在对图像进行压缩时,其压缩比是动态可调的,从而提出了一种新的方法,在图像压缩过程中能自适应的寻找最佳压缩比。文章引入能量差Q的概念,利用Q与奇异值个数q之间的关系,通过确定合适的Q值,动态寻找最佳的q值,从而达到动态图像压缩的目的。经过对多幅灰度图像的压缩实验证明,该方法切实可行,具有一定的应用前景。     

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。     

基于奇异值分解的图像降噪方法

由于奇异值分解可以有效地提取图像的主要特征,为了提高算法的鲁棒性,通过将矩阵的奇异值分解引入振幅-相位恢复算法,提出了基于奇异值分解的抗噪声多强度相位恢复算法。首先,将奇异值分解引入振幅-相位恢复算法,在振幅-相位恢复算法取平均值之后,对恢复的图像进行奇异值分解,奇异值较小的分量认为是噪声,保留奇异值较大的分量,将奇异值较小的分量置为0。其次,通过数值模拟实验可以看出,通过奇异值分解,不仅可以去除测量过程中所引入的噪声,而且还可以充分利用自然图像稀疏的特性,加快振幅-相位恢复算法的收敛,且具有更少的算法运行时间。     

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。     

一种基于奇异值分解的特征抽取方法

特征抽取是模式识别的基本问题之一,Fisher线性鉴别分析是特征抽取中最为经典和广泛使用的方法之一。该文分析了Fisher线性鉴别分析在求解过程中可能存在的问题:鉴别矢量的分量可能是复数;特征值对扰动的敏感性;鉴别矢量之间未必具有正交性。由此提出了均衡散布矩阵的概念,并利用均衡散布矩阵构造了一种新的线性鉴别准则。利用奇异值分解定理,将求取鉴别矢量转化为对矩阵求奇异向量。用该方法进行求解可以有效地避免前述的问题。试验结果表明,该鉴别准则具有良好的鉴别能力。     

基于尺度奇异值变换的红外图像目标特征提取方法研究

对实际空中红外图像目标的大小、方向等变化的情况,基于奇异值分解变换的特征向量稳定性不好。为解决这一技术难题,文中提出了基于尺度奇异值变换的红外图像目标特征提取方法,实验表明该方法提取的目标矩阵的奇异值特征向量具有较好的稳定性,对图像的平移、旋转和比例变化具有良好的不变性,是一种实用的特征提取方法。     

基于奇异值分解的数字图像水印方法

随着计算机和网络技术的飞速发展,数字图像、音频和视频产品愈来愈需要一种有效的版权保护方法,另外通信系统在网络环境下的信息安全问题也日益显露出来.数字图像水印技术为上述问题提供了一个潜在的解决方案.所谓水印技术就是将数字、序列号、文字、图像标志等版权信息嵌入到多媒体数据中,以起到版权保护、秘密通信、数据文件的真伪鉴别和产品标志等作用.本文提出了一种新的基于奇异值分解的数字水印算法并且对该方法的理论基础给出分析.实验结果表明这种方法要比目前提出的流行算法鲁棒.     

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。     

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.     

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。     

Hardware implementation of a novel water marking algorithm based on phase congruency and singular value decomposition technique

The effortless accessibility of digital information and the simplicity of the digital systems have left the contents over the digital media extremely insecure. Digital watermark based information hiding is a prospective means for copyright protection, authentication, integrity verification and intellectual property right protection. Phase congruency technique works on the principle that perceptually significant image features have effect at spatial locations, where the essential Fourier components are maximally in phase with one another. An adaptive digital watermarking algorithm for better performance in multi-parametric solution space is developed here for hiding the copyright information by means of phase congruency and singular value decomposition supported information hiding technique. Performance evaluation of the algorithm is performed using simulation in Matlab in terms of Peak Signal to Noise Ratio, Structural Similarity Index Metrics, and Normalized Cross correlation index. Hardware realization up to the register transfer logic schematic level has been performed using high performance field programmable gate array board. The device utilization is 26% only, the dynamic power consumption of the circuit is 5.029 mW and delay after clock is 1.539 ns only. The experimental analysis establishes better robustness of the proposed algorithm as it stands against various attacks along with better data hiding capacity.     

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.