基于小波域HMM模型的自适应图像水印算法

An adaptive image watermarking algorithm based on HMM in wavelet domain is proposed. The algorithm is abstracted as follows: 1) the vector HMM model is employed to describe the statistical characteristic of image wavelet coefficients and the resulting HMM based detector achieves significant improvements in performance compared to the conventional correlation detector; 2) adaptive watermark embedding based on HVS analysis; 3) a novel embedding strategy which is optimized for the HMM tree structure is adopted; 4) the strategy of dynamical threshold is applied in watermark detection. High robust results are achieved against Stirmark attacks, such as JPEG compression, adding noise, median cut and filter.     

基于颜色和特征匹配的视频图像人脸检测实现技术

A face detection method using statistical skin-color model and facial feature matching is presented in this paper.According to skin-color distribution in YUV color space,we develope a statistical skin-color model through interactive sample training and learning.Using this method we convert the color image to binary image and then segment face-candidate regions in the video images.In order to improve the quality of binary image and remove unwanted noises,filtering and mathematical morphology are empolied.After these two processing,we use facial feature matching for further detection.The presence or absence of a face in each region is verified by means of mouth detector based on a template matching method.The experimental results show the proposed method has the features of high speed and high efficiency,but also robust to face variation to some extent.So it is suitable to be applied to real-time face detection and tracking in video sequences.     

A Two-Stage State Recognition Method for Asynchronous SSVEP-Based Brain-Computer Interface System

A two-stage state recognition method is proposed for asynchronous SSVEP(steady-state visual evoked potential) based brain-computer interface(SBCI) system.The two-stage method is composed of the idle state(IS) detection and control state(CS) discrimination modules.Based on blind source separation and continuous wavelet transform techniques,the proposed method integrates functions of multi-electrode spatial filtering and feature extraction.In IS detection module,a method using the ensemble IS feature is proposed.In CS discrimination module,the ensemble CS feature is designed as feature vector for control intent classification.Further,performance comparisons are investigated among our IS detection module and other existing ones.Also the experimental results validate the satisfactory performance of our CS discrimination module.     

基于期望值最大算法和离散小波框架的图像融合

The discrete wavelet transform has become an attractive tool for fusing multisensor images. This paper investigates the discrete wavelet frame transform. A major advantage of this method over discrete wavelet transform is aliasing free and translation invariant. The discrete wavelet frame (DWF) transform is used to decompose the registered images into multiscale representation with the low frequency and the high frequency bands. The low frequency band is normalized and fused by using the expectation maximization (EM) algorithm. The informative importance measure is applied to the high frequency band. The final fused image is obtained by taking the inverse transform on the composite coefficient representations. Experiments show that the proposed method is more effective than conventional image fusion methods.     

Mammogram Enhancement Using Lifting Dyadic Wavelet Transform and Normalized Tsallis Entropy

In this paper, we present a new technique for mammogram enhancement using fast dyadic wavelet transform （FDyWT） based on lifted spline dyadic wavelets and normalized Tsallis entropy. First, a mammogram image is decom- posed into a multiscale hierarchy of low-subband and high-subband images using FDyWT. Then noise is suppressed using normalized Tsallis entropy of the local variance of the modulus of oriented high-subband images. After that, the wavelet coefficients of high-subbands are modified using a non-linear operator and finally the low-subband image at the first scale is modified with power law transformation to suppress background. Though FDyWT is shift-invariant and has better poten- tial for detecting singularities like edges, its performance depends on the choice of dyadic wavclcts. On the other hand, the nulnber of vanishing moments is an important characteristic of dyadic wavelets for singularity analysis because it provides an upper bound measurement for singularity characterization. Using lifting dyadic schemes, we construct lifted spline dyadic wavelets of different degrees with increased number of vanishing moments. We also examine the effect of these wavelets on mammogram enhancement. The method is tested on mammogram images, taken from MIAS （Mammographic Image Analysis Society） database, having various background tissue types and containing different abnormalities. The comparison with tile state-of-the-art contrast enhancement methods reveals that the proposed method performs better and the difference is statistically significant.     

Performance study of selective encryption in comparison to full encryption for still visual images

Securing digital images is becoming an important concern in today＇s information security due to the extensive use of secure images that are either transmitted over a network or stored on disks. Image encryption is the most effective way to fulfil confidentiality and protect the privacy of images. Nevertheless, owing to the large size and complex structure of digital images, the computational overhead and processing time needed to carry out full image encryption prove to be limiting factors that inhibit it of being used more heavily in real time. To solve this problem, many recent studies use the selective encryption approach to encrypt significant parts of images with a hope to reduce the eneryption overhead. However, it is necessary to realistically evaluate its performance compared to full encryption. In this paper, we study the performance and efficiency of image segmentation methods used in the selective encryption approach, such as edges and face detection methods, in determining the most important parts of visual images. Experiments were performed to analyse the computational results obtained by selective image encryption compared to full image encryption using symmetric encryption algorithms. Experiment results have proven that the selective encryption approach based on edge and face detection can significantly reduce the time of encrypting still visual images as compared to full encryption. Thus, this approach can be considered a good alternative in the implementation of real-time applications that require adequate security levels.     

Estimating fisheye camera parameters from homography

This paper presents a method to linearly estimate fisheye camera model parameters from the homography induced by the space plane between two fisheye images.The homography is firstly calculated by using four feature points,instead of three points on the same line,in the fisheye image.And the constraint on the model parameters of fisheye camera can be derived from the homography under the assumption that fisheye camera model is a polynomial model.Then the model parameters for different order polynomials are computed.The proposed technique requires only multiple fisheye images to include a planar scene and need not a priori knowledge of 3D coordinate of the planar scene.Experimental results with synthetic data and real fisheye images demonstrate the validity of our method.The method can also be extended to fisheye image of other planar scene excluding the planar calibration object.     

Thermodynamics-inspired inverse halftoning viamultiple halftone images

Based on an analogy between thermodynamics and Bayesian inference,inverse halftoning was formulated using multiple halftone images based on Bayesian inference using the maximizer of the posterior marginal(MPM) estimate.Applying Monte Carlo simulation to a set of snapshots of the Q-Ising model,it was demonstrated that optimal performance is achieved around the Bayes-optimal condition within statistical uncertainty and that the performance of the Bayes-optimal solution is superior to that of the maximum-a-posteriori(MAP) estimation which is a deterministic limit of the MPM estimate.These properties were qualitatively confirmed by the mean-field theory using an infinite-range model established in statistical mechanics.Additionally,a practical and useful method was constructed using the statistical mechanical iterative method via the Bethe approximation.Numerical simulations for a 256-grayscale standard image show that Bethe approximation works as good as the MPM estimation if the parameters are set appropriately.     

Fast adaptive wavelet for remote sensing image compression

Remote sensing images are hard to achieve high compression ratio because of their rich texture. By analyzing the influence of wavelet properties on image compression, this paper proposes wavelet construction rules and builds a new biorthogonal wavelet construction model with parameters. The model parameters are optimized by using genetic algorithm and adopting energy compaction as the optimization object function. In addition, in order to resolve the computation complexity problem of online construction, according to the image classification rule proposed in this paper we construct wavelets for different classes of images and implement the fast adaptive wavelet selection algorithm （FAWS）. Experimental results show wavelet bases of FAWS gain better compression performance than Daubechies9/7.     

Palmprint Recognition by Applying Wavelet-Based Kernel PCA

This paper presents a wavelet-based kernel Principal Component Analysis (PCA) method by integrating the Daubechies wavelet representation of palm images and the kernel PCA method for palmprint recognition. Kernel PCA is a technique for nonlinear dimension reduction of data with an underlying nonlinear spatial structure. The intensity values of the palmprint image are first normalized by using mean and standard deviation. The palmprint is then transformed into the wavelet domain to decompose palm images and the lowest resolution subband coeffcients are chosen for palm representation. The kernel PCA method is then applied to extract non-linear features from the subband coeffcients. Finally, similarity measurement is accomplished by using weighted Euclidean linear distance-based nearest neighbor classifier. Experimental results on PolyU Palmprint Databases demonstrate that the proposed approach achieves highly competitive performance with respect to the published palmprint recognition approaches.     

基于小波域广义高斯分布的SAR图像分割算法

针对利用灰度共生矩阵作为纹理特征的传统方法不能够有效表征图像的边缘高频信息的问题,结合小波的多分辨率分析,提出了一种基于小波变换域统计特性的合成孔径雷达（SAR）图像分割算法。图像经过小波变换后,其统计特性服从广义高斯分布（GGD）,利用最大似然（ML）估计,推导出GGD的两个参数[α]、[β],提出了利用Newton-Raphson法对[β]进行快速迭代求解。并将[α]、[β]作为SAR图像的纹理特征,利用K-Means对其进行分割。通过对典型的SAR图像结果分析,表明了该算法的有效性。     

基于双树复小波二元统计模型的图像去噪方法

为了更有效地进行图像去噪,提出了一种基于双树复小波二元统计模型的图像去噪方法,该方法先用带参数的二元广义高斯分布(GGD)来模拟原图双树复小波系数的统计分布;然后结合最大似然估计(MLE)得到优化的参数估计;最后在此先验分布的基础上,运用最大后验概率(MAP)来估计从噪声图的小波系数中恢复原图的系数,从而达到去噪的目的。实验表明该新方法不仅可以干净地去除图像的噪声,还可以有效地保留图像细节,取得了良好的去噪效果,尤其是去噪图像的视觉效果要明显优于目前的很多算法。     

M带小波的子带自适应阈值除噪算法

在广义高斯分布（Generalized Guassian Distribution,GGD）模型最优软阈值的基础上,提出了一种基于M带小波变换的子带自适应图像除噪阈值确定方法,在阈值确定中,考虑了尺度因子、子带大小等因素的影响。采用软阈值除噪,算法简单实用。实验表明,对纹理丰富的图像,该文提出的除噪方法效果优于目前流行的其他算法。     

基于冗余小波变换的医学超声图像去斑噪算法

医学超声图像中固有的斑点噪声严重降低了图像的可解译程度,影响了后续的图像分析和诊断。提出了一种基于冗余小波变换的超声图像去斑算法,首先对含斑图像进行对数变换,将乘性噪声变成加性噪声;再对转换后图像做冗余小波分解;在小波系数服从广义高斯分布的前提下,计算每个小波高频子带的贝叶斯萎缩阈值,利用软阈值方法修正小波系数。实验结果表明,该算法去斑性能优于传统的空间域滤波和正交小波阈值去噪方法。     

Visual quality recognition of nonwovens using generalized Gaussian density model and robust Bayesian neural network

This work is dedicated to develop an algorithm for the visual quality recognition of nonwoven materials, in which image analysis and neural network are involved in feature extraction and pattern recognition stage, respectively. During the feature extraction stage, each image is decomposed into four levels using the 9-7 bi-orthogonal wavelet base. Then the wavelet coefficients in each subband are independently modeled by the generalized Gaussian density (GGD) model to calculate the scale and shape parameters with maximum likelihood (ML) estimator as texture features. While for the recognition stage, the robust Bayesian neural network is employed to classify the 625 nonwoven samples into five visual quality grades, i.e., 125 samples for each grade. Finally, we carry out the outlier detection of the training set using the outlier probability and select the most suitable model structure and parameters from 40 Bayesian neural networks using the Occam's razor. When 18 relevant textural features are extracted for each sample based on the GGD model, the average recognition accuracy of the test set arranges from 88% to 98.4% according to the different number of the hidden neurons in the Bayesian neural network.     

使用统计模型的动态红外和可见光图像融合

针对动态红外和可见光图像融合,提出了一种新的基于统计模型的融合方法,即将图像的小波分解系数用广义高斯分布来建模。首先,源图像分别用双树复小波进行分解;然后,采用加权平均融合规则来进行小波系数的融合,其中加权系数由估计的广义高斯分布参数来计算;最后,将融合后的系数重构为一幅图像。融合图像采用熵、互信息和边缘保持度Q^AB/F来进行质量评价,实验结果表明方法的性能优于其他两种动态图像融合方法。     

基于MATLAB的图像小波子带广义高斯模型的研究

图像小波系数的统计分布具有非高斯特性，本文以采用广义高斯分布对图像子带小波系数进行建模为例，研究了广义高斯模型和模型参数的估计问题，介绍了利用MATLAB语言设计概率直方图以及求解模型参数超越方程的方法，最后对实验结果进行了讨论。     

基于弱信号检测理论的离散小波变换域数字水印盲检测算法

水印检测是数字水印技术的一个关键步骤，但是目前所提出的绝大多数水印检测算法既不具备赖以支撑的理论基础，其检测性能也不是优化的．利用数字水印的不可感知特性，应用弱信号检测理论进行水印检测的研究．首先从图像小波变换系数的统计特性出发，利用广义高斯分布（GGD）来建立小波细节子带系数的统计分布模型；然后针对水印嵌入强度未知的情况，将水印检测问题转化为非高斯噪声中具有未知参数的确定性信号的检测，在弱信号条件下提出一种离散小波变换（DWT）域图像水印的优化盲检测算法．检测性能分析表明，该检测算法能够实现恒虚警率水印检测．实验结果验证了检测性能理论分析的有效性和实际性能的优越性．     

基于小波变换和隐马尔可夫模型的人脸识别方法

提出了基于小波变换和隐马尔可夫模型的人脸识别方法。对原始图像采用小波分解后,原始图像被分解到不同的频带上。利用小波理论分析可知,在每一级分解中,低频子图像包含了原始图像的主要描述信息,而其他3个高频子图像包含的信息较少,对模式分类的作用也较小,所以可忽略不计。该算法首先对图像进行3级小波分解,然后把3个不同分辨率的低频子图像由小到大排列成树状结构,形成低频小波树。接着利用主元分析对每个小波树枝进行去相关、降维,形成特征小波树枝,并把它作为观测向量对隐马尔可夫模型进行训练,把优化的模型参数用于人脸识别,实验结果表明,该方法识别率较高,具有很好的发展前景。     

基于多小波子带加权判别熵的SAR目标ICA特征提取及识别

传统小波独立分量分析(ICA)提取合成孔径雷达(SAR)目标特征时大都采用单一的小波基函数,并且仅利用小波分解低频子带数据进行ICA处理,而忽略了高频子带信息。针对这一问题,采用多类小波基函数对SAR目标图像进行分解;针对得到的所有低频和高频子带数据,引入子带加权的判别熵准则,结合现有的小波ICA算法,提出多小波子带加权判别熵的SAR目标图像ICA特征提取算法。采用MSTAR实测SAR目标图像数据,根据提出算法进行特征抽取,利用最近邻准则进行SAR目标识别。识别结果表明提出算法优于仅利用小波分解低频子带ICA算法。