Probabilistic linear models for multiresolution estimation in gray-level images

A multiresolution analysis of digital gray-level images is presented. A gray-level multi-scale framework is determined from two main assumptions: the gray scale is binary at the finest spatial resolution, and the gray levels of composed regions are obtained additively. In order to interrelate the gray-level histograms of the same image at different resolutions, probabilistic linear models are developed, which are then applied for estimation. Linear-optimization theory is used as a way of constructing such models. A general procedure for image processing is sketched, based on gray-level estimation. A versatile algorithm for nonlinear filtering is derived. Some examples of prospective applications are given.This work was partially supported by grant TIC91-646 from the DGYCIT of the Spanish Government.     

Optimized analysis of the critical behavior in polymer mixtures from Monte Carlo simulations

A complete outline is given for how to determine the critical properties of polymer mixtures with extrapolation methods similar to the Ferrenberg-Swendsen techniques recently devised for spin systems. By measuring not only averages but the whole distribution of the quantities of interest, it is possible to extrapolate the data obtained in only a few simulations nearT _c over the entire critical region, thereby saving at least 90% of the computer time normally needed to locate susceptibility peaks or cumulant intersections and still getting more precise results. A complete picture of the critical properties of polymer mixtures in the thermodynamic limit is then obtained with finite-size scaling functions. Since the amount of information extracted from a simulation in this way is drastically increased as compared to conventional methods, the investigation of mixtures with long chains or built-in asymmetries is now possible. As an example, the critical points, exponents, and amplitudes of dense, symmetric polymer mixtures with chain lengths ranging fromN=16 up toN=256 are determined within the framework of the 3D bond fluctuation model using grand canonical simulation techniques. As an example for an asymmetry, the generalization of the method to asymmetric monomer potentials is briefly discussed.     

一种基于连续波雷达的手势识别方法

为了解决依靠光学传感器进行手势识别对外部环境依赖较大的问题,提出了一种基于连续波(Continuous Wave,CW)雷达的手势识别方法,并建立了4种手势动作的回波数据库.首先,对CW雷达回波进行短时傅里叶变换(Short-Time Fourier Transform,STFT)获取手势动作的时频谱;然后,通过设立阈...     

基于直方图的音频信息隐藏检测

基于信息隐藏引起的幅度直方图变化,提出了一种音频位平面信息隐藏检测方法.通过该方法不仅可以检测待测音频是否包含隐藏信息,还能识别信息嵌入位平面.该方法已在计算机上进行了实验,结果表明,对连续和随机间隔嵌入算法均有较高检测率.     

彩色立体像对的自适应匹配

提出了一种自适应权值的色彩相似性度量方法,有效地抑制了噪声对立体匹配的影响,提出以立体像对左、右图像间的颜色直方图相关系数为标准来调整权值的方法,并通过实验推导出具体的计算公式.对以灰度变化为基准的自适应窗选择方法进行改进,在边界提取过程中加入了彩色信息,使得提取的物体轮廓更为完整;用"米"字型窗口来代替原来的矩形窗口也使得窗口增长更为灵活,获得的匹配更为准确.实验结果表明,本文算法能够生成精确度较高的视差图,是一种较好的立体匹配算法.     

一种大容量的图像可逆信息隐藏算法

提出了一种大容量的可逆信息隐藏算法的详细实现方案.利用图像直方图中的多个零点或者最小点,通过修改图像像素的灰度值来隐藏信息,并通过统计分析给出常用的信息隐藏的模型和算法.实验表明,该方法不仅可以进行大容量的信息隐藏,而且相对原始图像能够达到满意的峰值信噪比(PSNR),具有良好的性能.     

基于小波域热红外降质图像滤波增强方法研究

随着现代网络传输和视频通信技术的快速发展,大大拓宽了热红外图像的应用领域.提出了一种基于小波变换的热红外将质图像滤波增强方法.该方法以受到不同强度高斯白噪声和脉冲噪声构成的混合噪声模型的热红外降质图像为研究对象,首先对其进行二维小波变换,从而获得高频和低频小波分解系数;鉴于低频小波分解系数包含图像大部分信息,基本不受噪声干扰的特点,引入直方图均衡化法进行增强处理,以改善图像低频信息的对比度;根据各方向的小波高频分解系数中噪声的分布特征,对经典数学形态学滤波算法进行研究,分别设计出几类多尺度多方向的结构元素,实现对各高频小波分解系数中噪声的三级串联滤波处理,在此基础上进行自适应同态滤波增强,以最大限度改善滤波后图像质量.最后进行小波分解系数重构.实验结果表明,该算法对于热红外将质图像的处理效果优于单纯进行经典数学形态学滤波和已有的改进数学形态学滤波,为该类降质图像的滤波增强处理提供了一条可供借鉴的思路.     

选择性背景优先的显著性检测模型

在检测图像显著性区域的领域中,背景优先是一个较新的思路,但会遇到背景鉴别这个具有挑战性的难题。该文提出背景真实性的判断问题,在探索的过程中发现背景通常具有连续性的特征,根据这一特性实现了判定背景的方法,并将判断的结果作为显著性先验值应用于后继的计算中,最终结果的准确性和正确性得到有效提高。该文首先采用均值漂移(MS)分割算法将图片预分为超像素,计算所有超像素的初始显著值;随后提取原图的4个边界条,计算每两条之间的色彩直方图距离,判定小于预设阈值的两条边界作为真的背景,选择它们作为优先边界,计算先验显著性值;最后进行显著性计算,得到最终的显著图。实验结果表明,该算法能够准确检测出显著性区域,与其他6种算法相比具有较大优势。     

Adaptive reversible data hiding for JPEG images with multiple two-dimensional histograms

Joint photographic experts group (JPEG) can provide good quality with small file size but also eliminate extensively the redundancies of images. Therefore, hiding data into JPEG images in terms of maintaining high visual quality at small file sizes has been a great challenge for researchers. In this paper, an adaptive reversible data hiding method for JPEG images containing multiple two-dimensional (2D) histograms is proposed. Adaptability is mainly reflected in three aspects. The first one is to preferentially select sharper histograms for data embedding after $K$ histograms are established by constructing the $k$th ($k\in \{1,2,\dots ,K\}$) histogram using the $k$th non-zero alternating current (AC) coefficient of all the quantized discrete cosine transform blocks. On the other hand, to fully exploit the strong correlation between coefficients of one histogram, the smoothness of each coefficient is estimated by a block smoothness estimator so that a sharply-distributed 2D-histogram is constructed by combining two coefficients with similar smoothness into a pair. The pair corresponding to low complexity is selected priorly for data embedding, leading to high embedding performance while maintaining low file size. Besides, we design multiple embedding strategies to adaptively select the embedding strategy for each 2D histogram. Experimental results demonstrate that the proposed method can achieve higher rate–distortion performance which maintaining lower file storage space, compared with previous studies.