求解混沌映射不变分布的区间迭代算法

从Frobeniui—Perron积分方程出发，将混沌映射的不变分布看成Forbenius-Perron算子的不动点，经过简化后得到了该不动点的逼近迭代式。在样本数较少的情况下，采用区间迭代算法完成了不变分布的迭代逼近。该方法克服了直方图法要求样本数目大，统计误差明显的缺点，特别适用于分析样本数受限的实际系统。     

基于极线校正的亚像素相位立体匹配方法

针对双目结构投影光栅相位法三维测量系统中立体匹配的效率与精度问题,提出了基于极线校正的亚像素相位立体匹配方法。首先,通过外极线校正算法将双目立体视觉几何结构校正为平视双目标准几何结构,使匹配点对在左右相机上的成像点位于同一水平线上。然后,采用双线性内插法获取校正后新相位图像对,并提出一种基于相位区域匹配的立体匹配算法,获得初始匹配点。最后,提出采用最小二乘拟合将初始匹配点附近3×3邻域内的相似度量拟合为一个二次曲面,然后求解得到该曲面的局部最小值,得到与待匹配点相位相同的右摄像机匹配点坐标。通过对标准平板平面度进行测量与精度分析,表明该方法能够快速、准确的实现立体匹配。     

基于稀疏和低秩表示的显著性目标检测

文中研究了无监督自下而上的显著性目标检测方法。基于显著性目标在自然图像中稀疏分布的这一先验性假设,提出了一种用低秩和稀疏表示进行显著性目标检测的方法。根据图像背景的先验分布,首先选取一个有效的背景字典来低秩表示图像的背景部分,进而更好地分离出显著性前景。由于人类视觉中心偏好可知,图像的边缘部分不易引起关注,故选取这些边缘部分作为背景先验来选取背景字典。与其他基于稀疏和低秩分解的显著性目标检测相比,文中选取的背景字典更简单有效,且能得到更好的显著性图。实验结果显示,该方法比主流的显著性检测方法得到的显著性图更令人满意。     

基于一种特定混沌映射的跳频序列

使用一种简单的分段线性映射构造混沌跳频序列。用于跳频通信中取代传统的伪随机序列（PN序列），分析了此混沌序列的周期性。平衡性，汉明相关特性和线性间隔，最后给出了混沌序列的神经网络实现。仿真和实验结果表明，此序列具有较好的统计特性。应用于跳频多址通信中，可获得较好的抗干扰。抗截获能力。     

基于混沌参数调制的数据加密算法

讨论了攻击混沌参数调制保密系统的两种策略,在此基础上提出一种两级调制的混沌映射数据加密算法,其中给数据又增加了一层反破解的混沌保护外壳.该方案生成的密文序列具有很强的复杂性,能隐藏参数数字调制的特征.在分析数据加密的过程中给出了仿真结果.     

基于Logistic混沌系统与魔方变换的图像加密方案

设计了一种基于Logistic映射与魔方变换的图像加密方案。该方案采用改进的混沌序列二值化方法,并借用魔方变换的思想,将二值序列转换所得的整数序列与图像像素值运算后,对图像的行列分别作循环移位完成对图像的加密。实验结果分析表明,该加密方案具有良好的图像加密效果,对于常见的攻击具有较强的抗干扰能力。     

Analysis of Volatile Bread Aroma for Evaluation of Bread Freshness Using an Electronic Nose (E-Nose)

This paper presents the use of a tin-oxide sensor array and self-organized map (SOM)-based E-nose for analysis of volatile bread aroma and explores its ability to cluster bread odor data according to the freshness of bread. A low cost tin-oxide sensor array based electronic nose system has been used for the classification of state of freshness of bread. The sensor data was acquired for a period of 3 weeks, and an unsupervised self-organizing map (SOM) model was trained using this data to correlate the sensor response to classify the bread as fresh and stale. A comparative evaluation of 3 week' of bread data was carried out using the SOM. The results suggest that the system developed is able to predict the state of bread as fresh and stale up to 98% accuracy if the test bread data sets are of the same week. The classification accuracy reduces to 75-85% if test bread data sets are from different weeks. The model is also applied on three different brands of bread and similar classification results are obtained.     

基于广义猫映射和加法模运算的快速图像加密系统

二维的混沌映射因其双瞳剪水初值敏感性以及伪随机性而广泛应用于图像加密.本文提出一种快速的图像加密系统,利用扩展的猫映射对图像进行置乱,通过简单的加法模运算对像素的灰度值进行替代与扩散,并且在每一轮迭代中采用不同的密钥.     

基于自组织映射和模糊隶属度的混合像元分解

遥感图像中普遍存在着混合像元,将混合像元分解为端元和它们之间混合的丰度,对于高精度的地物识别和定量遥感具有重要意义.结合自组织映射神经网络和模糊理论中的模糊隶属度,提出一种新的多光谱和高光谱遥感图像混合像元分解的方法.首先对自组织映射神经网络进行有监督的训练,然后基于模糊模型对混合像元进行分解.其分解结果自动满足混合像元分解问题所要求的2个约束:丰度值非负约束及丰度值和为1约束.实验结果表明,该方法不仅适用于线性光谱混合的情况,也适用于非线性光谱混合的情况,能够获得较好的混合像元分解结果,同时具有较强的抗噪声能力.     

Rapid Anisotropic Diffusion Using Space-Variant Vision

Many computer and robot vision applications require multi-scale image analysis. Classically, this has been accomplished through the use of a linear scale-space, which is constructed by convolution of visual input with Gaussian kernels of varying size (scale). This has been shown to be equivalent to the solution of a linear diffusion equation on an infinite domain, as the Gaussian is the Green's function of such a system (Koenderink, 1984). Recently, much work has been focused on the use of a variable conductance function resulting in anisotropic diffusion described by a nonlinear partial differential equation (PDE). The use of anisotropic diffusion with a conductance coefficient which is a decreasing function of the gradient magnitude has been shown to enhance edges, while decreasing some types of noise (Perona and Malik, 1987). Unfortunately, the solution of the anisotropic diffusion equation requires the numerical integration of a nonlinear PDE which is a costly process when carried out on a uniform mesh such as a typical image. In this paper we show that the complex log transformation, variants of which are universally used in mammalian retino-cortical systems, allows the nonlinear diffusion equation to be integrated at exponentially enhanced rates due to the nonuniform mesh spacing inherent in the log domain. The enhanced integration rates, coupled with the intrinsic compression of the complex log transformation, yields a speed increase of between two and three orders of magnitude, providing a means of performing rapid image enhancement using anisotropic diffusion.