A New Digital Image Watermarking Algorithm Resilient to Desynchronization Attacks

Synchronization is crucial to design a robust image watermarking scheme. In this paper, a novel feature-based image watermarking scheme against desynchronization attacks is proposed. The robust feature points, which can survive various signal-processing and affine transformation, are extracted by using the Harris-Laplace detector. A local characteristic region (LCR) construction method based on the scale-space representation of an image is considered for watermarking. At each LCR, the digital watermark is repeatedly embedded by modulating the magnitudes of discrete Fourier transform coefficients. In watermark detection, the digital watermark can be recovered by maximum membership criterion. Simulation results show that the proposed scheme is invisible and robust against common signal processing, such as median filtering, sharpening, noise adding, JPEG compression, etc., and desynchronization attacks, such as rotation, scaling, translation, row or column removal, cropping, and random bend attack, etc.     

A robust content based image watermarking using local invariant histogram

Desynchronization attack is known as one of the most difficult attacks to resist, which can desynchronize the location of the watermark and hence causes incorrect watermark detection. Based on multi-scale SIFT (Scale Invariant Feature Transform) detector and local image histogram shape invariance, we propose a new content based image watermarking algorithm with good visual quality and reasonable resistance toward desynchronization attacks in this paper. Firstly, the stable image feature points are extracted from the original host by using multi-scale SIFT detector, and the local feature regions (LFRs) are constructed adaptively according to the feature scale theory. Then, the discrete Fourier transform (DFT) is performed on the LFR, and the local image histogram is extracted from a selected DFT amplitude range. Finally, the bins of the histogram are divided into many groups, and the digital watermark is embedded into LFR by reassigning the number of DFT amplitudes in bin groups. By binding the watermark with the geometrically invariant image features, the watermark detection can be done without synchronization error. Experimental results show that the proposed image watermarking is not only invisible and robust against common image processing operations such as sharpening, noise adding, and JPEG compression, but also robust against the desynchronization attacks such as rotation, translation, scaling, row or column removal, and cropping.     

基于蚁群算法的图像分割方法

蚁群算法是一种具有离散性?并行性?鲁棒性和模糊聚类能力的进化方法?根据数字图像的离散性特点,首先从模糊聚类角度出发,将蚁群算法引入图像分割中,综合考虑像素的灰度?梯度及邻域特性进行特征提取?然后,针对蚁群算法循环次数多,计算量大的问题,设置启发式引导函数和初始聚类中心进行改进?详细阐述特征提取?初始聚类中心设置和模糊聚类流程?实验证明改进蚁群算法可以快速准确地分割出目标,是一种有效的图像分割方法     

分数傅里叶域图像数字水印方案

根据离散分数傅里叶变换（DFRFT），提出了一种基于分数傅里叶变换的图像数字水印方案。分数傅里叶变换具有空域和频城双城表达能力，可以对原始图像和水印信号分别进行不同阶次的分数傅里叶变换以增强水印安全性。将水印信号的分数傅里叶谱叠加在原始图像在视觉上的次重要分量上。在JPEG压缩、图像旋转、高斯低通滤波的攻击方式下，对水印图像进行了鲁棒性分析，实验表明该算法具有良好的鲁棒性。     

An edge-based scale- and affine-invariant algorithm for remote sensing image registration

For remote sensing image registration, we find that affine transformation is suitable to describe the mapping between images. Based on the scale-invariant feature transform (SIFT), affine-SIFT (ASIFT) is capable of detecting and matching scale- and affine-invariant features. Unlike the blob feature detected in SIFT and ASIFT, a scale-invariant edge-based matching operator is employed in our new method. To find the local features, we first extract edges with a multi-scale edge detector, then the distinctive features (we call these ‘feature from edge’ or FFE) with computed scale are detected, and finally a new matching scheme is introduced for image registration. The algorithm incorporates principal component analysis (PCA) to ease the computational burden, and its affine invariance is embedded by discrete sampling as ASIFT. We present our analysis based on multi-sensor, multi-temporal, and different viewpoint images. The operator shows the potential to become a robust alternative for point-feature-based registration of remote-sensing images as subpixel registration consistency is achieved. We also show that using the proposed edge-based scale- and affine-invariant algorithm (EBSA) results in a significant speedup and fewer false matching pairs compared to the original ASIFT operator.     

Non-blind digital watermarking with enhanced image embedding capacity using DMeyer wavelet decomposition,SVD, and DFT

Amongst the requirements of digital color image watermarking–capacity is the major component to be addressed effectively. To address the same we proposed a method for inserting a color image into another color image of same size using non-blind watermarking scheme. From this method we achieved reasonably good perceptual similarity by measuring acceptable peak signal to noise ratio (PSNR) and structural similarity (SSIM) index. The method uses DMeyer single level discrete wavelet transformation (DWT) to get approximation coefficients-where most of the image information is stored, discrete Fourier transformation (DFT) is used to get set of components which are sufficient to describe the whole image and singular value decomposition (SVD) to get reliable orthogonal matrix of computationally sustainable components of the transformed image. The method is robust against attacks like–rotation, cropping, JPEG compression and for noises–salt and pepper, gaussian, speckle.     

一个基于矢量量化的数字水印算法

文章提出了一个新的基于矢量量化的数字水印算法,与基于DCT(DiscreteCosineTransform)、DFT(DiscreteFourierTransform)及DWT(DiscreteWaveletTransform)等的传统水印算法不同,该算法利用码书分割方法和矢量量化索引的特点,在矢量量化的不同阶段分别嵌入水印来保护原始图像的版权,水印检测不需要原始图像。实验结果表明,该方法实现的水印具有良好的不可见性,并对JPEG压缩、矢量量化压缩、旋转以及剪切等空域操作也具有较好的稳健性。     

基于离散傅里叶不变特征的人脸识别

给出了一种基于离散傅里叶不变特征的人脸识别方法。从连续傅里叶变换出发,讨论连续傅里叶变换情况下的傅里叶变换性质,给出离散傅里叶变换情况下的傅里叶变换性质。依据离散傅里叶变换性质,推导出离散傅里叶变换的不变特征,并将其用于人脸图像识别。人脸识别结果表明方法具有很好的识别能力。     

FFT-ETM based distortion less and high payload image steganography

This paper is presenting a novel high capacity based imperceptible and robust image steganography technique for obscured communication. A considerable literature studied on this domain reveals distortion that drastically affects image quality. These techniques obscure covert data in most significant bits or least significant bits of host image via easy or unsystematic replacement. Such schemes are vulnerable to malevolent attacks like sample pair method, chi-square test, and quality of host image especially badly affected by MSB replacement. Furthermore, such schemes are lacking in carrying maximum covert information as the number of host image pixels and covert image pixels has the ratio 8:1. In our proposed scheme robust and imperceptibility feature is injected using insignificant pixel value divergence of host and a high capacity covert image. We have proposed frequency entropy method that compares frequencies of covert image and host image in FFT (Fast Fourier Transform) domain. The eminent rate of frequency ETM (Entropy Threshold Match) leads to good image quality and information carrying capability. Moreover, our proposed technique also encrypts the secret image in frequency domain with multi flipped permutated random key vector that provides robustness. Therefore, experiments exhibit that this scheme has improved signal to noise ratio and BPP (bits per pixel) in contrast to existing schemes.

     

基于小波与分数傅里叶变换的图像水印算法

载体图像的空域隐藏Chirp信号可以通过分数傅里叶变换在变换域中进行盲检测。为了提高该算法的鲁棒性能,该文研究直接离散化方法,合理选取分数傅里叶变换的算子阶数,将Chirp 信号隐藏在图像信号的低频小波域中。仿真实验表明,改进后的水印算法提高了直接在空域进行信息隐藏的鲁棒性。     

基于SURF特征和Delaunay三角网格的图像匹配

图像特征匹配的核心是通过距离函数实现在高维矢量空间进行相似性检索.重点研究提取好的特征点并快速准确地找到查询点的近邻.首先,提取图像的多量、有区别且稳健的SURF（Speeded up robust feature）特征点,并将特征点凸包进行Delaunay剖分.然后,对Delaunay三角边抽样、聚类、量化并构建索引.通过票决算法,将点对匹配与否映射到矩阵中以解决距离度量没有利用数据集本身所蕴含的任何结构信息和搜索效率相对较低的问题.结合SURF算法和Delaunay三角网提出一种特征匹配的新方法,在标准图像集上的实验验证,在耗时基本相同的情况下,提取的特征点较多且正确匹配率较高.     

Automatic classification of computed tomography brain images using ANN,k-NN and SVM

Computed tomography images are widely used in the diagnosis of intracranial hematoma and hemorrhage. This paper presents a new approach for automated diagnosis based on classification of the normal and abnormal images of computed tomography. The computed tomography images used in the classification consists of non-enhanced computed tomography images. The proposed method consists of four stages namely pre-processing, feature extraction, feature reduction and classification. The discrete wavelet transform coefficients are the features extracted in this method. The essential coefficients are selected by the principal component analysis. The features derived are used to train the binary classifier, which infer automatically whether the image is that of a normal brain or a pathological brain, suffering from brain lesion. The proposed method has been evaluated on a dataset of 80 images. A classification with a success of 92, 97 and 98 % has been obtained by artificial neural network, k-nearest neighbor and support vector machine, respectively. This result shows that the proposed technique is robust and effective.     

Feature line extraction from unorganized noisy point clouds using truncated Fourier series

The detection of feature lines is important for representing and understanding geometric features of 3D models. In this paper, we introduce a new and robust method for extracting feature lines from unorganized point clouds. We use a one-dimensional truncated Fourier series for detecting feature points. Each point and its neighbors are approximated along the principal directions by using the truncated Fourier series, and the curvature of the point is computed from the approximated curves. The Fourier coefficients are computed by Fast Fourier Transform (FFT). We apply low-pass filtering to remove noise and to compute the curvature of the point robustly. For extracting feature points from the detected potential feature points, the potential feature points are thinned using a curvature weighted Laplacian-like smoothing method. The feature lines are constructed through growing extracted points and then projected onto the original point cloud. The efficiency and robustness of our approach is illustrated by several experimental results.     

Finger-vein authentication based on deformation-tolerant feature-point matching

A novel method for finger-vein authentication based on feature-point matching is proposed and evaluated. A finger-vein image captured by infrared light contains artifacts such as irregular shading and vein posture deformation that can degrade accuracy of finger-vein authentication. Therefore, a method is proposed for extracting features from vein patterns and for matching feature points that is robust against irregular shading and vein deformation. In the proposed method, curvature of image-intensity profiles is used for feature point extraction because such image profiles are a robust feature against irregular shading. To increase the number of feature points, these points are extracted from any positions where vein shape is non-linear. Moreover, a finger-shape model and non-rigid registration method are proposed. Both the model and the registration method correct a deformation caused by the finger-posture change. It is experimentally shown that the proposed method achieves more robust matching than conventional methods. Furthermore, experiments on finger-vein identification show that the proposed method provides higher identification accuracy than conventional methods.     

Geometric properties estimation from discrete curves using discrete derivatives

Accurate geometric properties estimation from discrete curves is an important problem in many application domains, such as computer vision, pattern recognition, image processing, and geometric modeling. In this paper, we propose a novel method for estimating the geometric properties from discrete curves based on derivative estimation. We develop derivative estimation by defining the derivative of a discrete function at a point, which will be called the discrete derivative. Similarly, the second and higher order discrete derivatives at that point are also defined, and their convergence is demonstrated by theory analysis. These definitions of the different order discrete derivatives provide a simple and reliable way to estimate the derivatives from discrete curves. Based on the discrete derivatives, classical differential geometry can be discretized, and the geometric properties are estimated from discrete curves by using differential geometry theory. The proposed method is independent of any analytic curve and estimates the geometric properties directly from discrete data points, which makes it robust to the geometric shapes of discrete curves. Another advantage of the proposed method is the robustness to noise because of the calculation characteristics of the discrete derivatives. The proposed method is evaluated and compared with other existing methods in the experiments with both synthetic and real discrete curves. The test results show that the proposed method has good performance, and is robust to noise and suitable for different curve shapes.     

离散曲面的近似Poisson盘采样

Poisson盘采样作为计算机图形学的一个重要课题,在重网格化、过程纹理、物体分布、光照计算等方面都有重要应用.虽然最近几年对于2维平面Poisson盘采样的研究比较密集,但是直接对于2维流形表面上的Poisson盘采样的研究却比较少.在本文中,我们提出了一种可以直接在Mesh表面生成近似Poisson盘分布的方法.此方法实现简单,同时可以通过简单修改适用于保特征的采样和自适应采样.文中引入了张量投票的方法来实现特征识别和自适应采样半径的计算,并给出了采样后的重网格化结果,作为此算法的一个后期应用.通过大量实例表明,本文方法快速、鲁棒、适用广泛.     

A robust high-resolution details preserving denoising algorithm for meshes

Poisson disk sampling has been widely used in many applications such as remeshing, procedural texturing, object distribution, illumination, etc. While 2D Poisson disk sampling is intensively studied in recent years, direct Poisson disk sampling on 2-manifold surface is rarely covered. In this paper, we present a novel framework which generates approximate Poisson disk distribution directly on mesh, a discrete representation of 2-manifold surfaces. Our framework is easy to implement and provides extra flexibility to specified sampling issues like feature-preserving sampling and adaptive sampling. We integrate the tensor voting method into feature detection and adaptive sample radius calculation. Remeshing as a special downstream application is also addressed. According to our experiment results, our framework is efficient, robust, and widely applicable.     

基于离散余弦变换基函数迭代的人脸图像识别

使用非线性混沌方法处理与识别图像的研究工作逐渐增多,已有文献给出了一种 将正弦函数作为辅助函数与图像构造动力系统,迭代生成混沌吸引子作为图像特征。为进一步 探究图像吸引子作为图像的特性,改进识别效果,使用离散余弦变换(DCT)基函数矩阵代替正 弦函数,迭代生成近似混沌吸引子,用于人脸识别。首先,研究分析了DCT 基函数矩阵的多样 性与振荡特性;然后利用DCT 基函数矩阵与图像矩阵构造迭代表达式,通过给出的迭代算法使 其产生吸引子,再对吸引子进行快速傅里叶变换,计算相关系数,识别人脸图像。对于Yalefaces 图像库,每幅图像都参加训练,识别率可以达到100%,当使用每组前5 幅图像训练提取特征, 识别率可以超过85%;对于CMU PIE 数据库,每幅图像都参加训练,识别率可以超过99%。 该吸引子方法可以作为一种图像底层特征提取方法,有待于进一步深入研究。     

Novel infrared and visible image fusion method based on independent component analysis

The goal of infrared (IR) and visible image fusion is for the fused image to contain IR object features from the IR image and retain the visual details provided by the visible image. The disadvantage of traditional fusion method based on independent component analysis (ICA) is that the primary feature information that describes the IR objects and the secondary feature information in the IR image are fused into the fused image. Secondary feature information can depress the visual effect of the fused image. A novel ICA-based IR and visible image fusion scheme is proposed in this paper. ICA is employed to extract features from the infrared image, and then the primary and secondary features are distinguished by the kurtosis information of the ICA base coefficients. The secondary features of the IR image are discarded during fusion. The fused image is obtained by fusing primary features into the visible image. Experimental results show that the proposed method can provide better perception effect.