基于多特征差异检测与联合控制映射的红外图像选择加密算法

为了实现对红外图像的选择性加密,提出了基于多特征差异检测与联合控制映射的红外图像选择算法。引入分段正弦变换,将输出图像分割为3个不同的区域,对每个区域完成不同的拉伸变换,完成初始红外图像的增强,凸显真实红外目标;再利用增强图像中目标与背景的灰度差异,从而设计目标决策因子,并分割Top-Hat变换的结构元素,构建红外背景抑制机制,过滤杂波与噪声;利用灰度水平、对比度与相似度,建立多特征差异检测模型,提取包含真实目标与可疑目标的感兴趣区域;以Logistic映射为控制条件,综合Tent映射与Chebyshev映射,设计联合控制混沌映射,利用其输出的混合随机序列对感兴趣区域进行置乱;引入引力模型,对混淆的感兴趣区域内的像素进行扩散,完成红外目标选择加密。实验结果显示:与已有的图像局部加密机制相比,该文算法输出密文信息熵值达到了7.982 6,能够更好地用于红外图像局部选择性加密。     

一种新的数字图像加密算法

利用混沌原理,提出了一种新的二维可逆混沌映射,通过对图像的拉伸和折叠处理,实现了图像的混沌加密。首先按照扫描与插入原则将原始图像拉伸为一条直线,然后按照原始图像的大小,将直线折叠为一个新的图像。映射包括左映射和右映射两个子映射,将密钥设计为二维混沌映射的左映射和右映射的组合。仿真结果表明,图像在加密与解密前后没有信息缺失,且具有较好的安全性。     

一种新的可逆二维混沌映射图像加密算法

提出了一种用于图像加密的可逆二维混沌映射,该映射由左映射和右映射两个子映射组成。通过对图像的拉伸和折叠处理,实现了图像的混沌加密。首先沿图像的对角线方向将正方形图分为上下两个部分并重新组合成一个平行四边形的图像;然后利用平行四边形图像的两列像素之间的像素数目差将某列中的像素插入到相邻下一列像素之间。经过这样的过程,原始图像拉伸成为一条直线。最后按照原始图像的大小将这条直线折叠成为一个新的图像。推导出了映射的数学表达式,设计了密钥产生的方法,分析了图像加密算法的安全性问题。仿真验证了该图像加密算法的有效性。     

三维可逆混沌映射的图像加密算法

提出了一种三维可逆混沌映射图像加密算法。基于Line map二维混沌可逆映射,推导了该三维可逆映射的数学表达式。将灰度图像用一个三维矩阵数据描述,并按照所提出的算法将其组成一个二维的二进制图像。首先对此图像应用Line map二维混沌可逆映射进行像素置乱处理,然后再将置乱后的二进制图像还原成十进制的灰度图像,这样就得到了加密后的图像。所提出的方法可以通过一次三维可逆混沌映射同时实现图像加密的两个步骤,即像素置乱和像素混淆。仿真实验结果表明了该算法的有效性,且加密速度快、安全性高、简单易行。     

基于SIFT图像特征区域的全息水印技术

为了增强水印的不可见性和鲁棒性,提出了一种基于图像特征区域的水印算法。首先利用SIFT(Scale Invariant Feature Transform)算法从载体图像蓝色B通道中提取图像特征点来进行优化和筛选,根据优化后的稳定特征点及其特征尺度确定图像的特征区域,再结合全息技术,对原始水印图像进行双随机相位加密,生成加密全息水印;然后对特征区域进行离散余弦变换(DCT);最后在其中频区域嵌入加密全息水印。在提取水印时无须借助原始图像,是盲水印技术。实验结果表明:该算法重建的水印图像与原始水印图像的NC值高达0.95;水印的嵌入对图像质量影响很小,PSNR值高达55.97,能够抵抗常规信号攻击及缩放、剪切、平移等几何攻击。     

基于EZW的图像压缩和树形加密同步算法

为了实现图像的压缩和加密同步提出一种基于内嵌零树小波编码 (embedded zerotree wavelet, EZW) 的压缩和树形加密算法. 加密过程在比特平面编码与熵编码之间进行, 使用密钥对图像压缩产生的上下文和判决进行修正, 实现压缩和加密同步. 对算法进行仿真, 结果表明, 算法具有良好的压缩效率和安全性. 关键词： 图像压缩 图像加密 内嵌零树小波编码(EZW) Logistic映射     

基于混沌映射的图像快速加密改进算法

针对当前的三维混沌映射加密算法存在安全性不高,加密速度慢以及密钥空间小等不足,提出了一种新的三维混沌映射图像加密算法;首先利用快速置乱方法置乱初始图像,以改变像素位置;利用三维Chen系统结合像素值变换函数所生成初始外部密钥迭代三维混沌映射,得到一个序列,由此根据混淆机制对置乱图像像素值进行混淆;改变外部密钥,再迭代计算三维混沌映射,得到三元一维伪随机数组,借助密钥流机制量化该数组,得到新数组,由此根据扩散机制对混淆后的像素进行扩散处理;采用酷睿3.5 GHz双核CPU的PC机和MATLAB仿真平台,输入256×256的明文图像实验,置乱100次所用时间为78.67 s,在灰度平面内其相关性约为-0.001 652,表明该算法高度安全,密钥空间巨大,加密速度快,用于图像快速加密是可行、有效的。     

光学图像加密中随机模板的特性比较

为比较光学图像加密中随机模板的特性,分别利用随机相位模板和随机振幅模板对几种典型的光学图像加密系统进行了光学图像加密的数值模拟和加密效果分析。模拟结果表明,将随机振幅模板用于基于光学傅里叶变换的双随机模板图像加密系统时,原始图像和加密图像的相关系数大于05,不能有效加密原始图。但将其用于基于菲涅耳变换全息的光学图像加密系统时,原始图像和加密图像的相关系数趋于0,可获得与使用随机相位模板时非常接近的加密效果。     

光学图像加密中随机模板的特性比较

为比较光学图像加密中随机模板的特性,分别利用随机相位模板和随机振幅模板对几种典型的光学图像加密系统进行了光学图像加密的数值模拟和加密效果分析。模拟结果表明,将随机振幅模板用于基于光学傅里叶变换的双随机模板图像加密系统时,原始图像和加密图像的相关系数大于0.5,不能有效加密原始图。但将其用于基于菲涅耳变换全息的光学图像加密系统时,原始图像和加密图像的相关系数趋于0,可获得与使用随机相位模板时非常接近的加密效果。     

基于核映射多光谱特征融合的高光谱遥感图像分类法

多光谱遥感图像的波段设定在理论和实践上都极具地物针对性,在更有效的数据挖掘方法帮助下可以提取足够的光谱特征以区分地物,本文将光谱匹配技术用于相似性度量,以提高分类准确度.首先选定光谱角制图、光谱相关制图、Mahalanobis距离、光谱相似度和光谱信息差异做为光谱度量;随后选择高斯核函数,在选定核函数之后,得到了核映射下的光谱特征度量来挖掘高光谱遥感数据的光谱特征.最后采用核映射多光谱特征融合法对多光谱遥感图像光谱特征的相似性进行描述,得到基于核映射多光谱特征融合的高光谱遥感图像分类算法.使用MATLAB中的LIBSVM工具箱对AVIRIS高光谱遥感数据进行分类实验,并与已有算法进行对比,结果表明本文提出的算法具有较高的分类准确度和性能.     

Finger vein recognition algorithm based on optimized GHT

In this paper, we present a novel algorithm for recognition of vein features based on optimized generalized Hough transform (GHT). The new algorithm involves several steps. First, it extracts singular points from the binary image of finger veins, and segments the finger veins by these points. Then it selects valid segments and sequences them by way of chain codes. Next it uses the optimized GHT to differentiate sectional curves of finger veins from the whole finger vein image. Using this approach reduces the influence of fragmentation, enhances adaptability for displacement, rotation, and zooming, and accordingly improves the quality of finger vein recognition. We have tested the proposed method with actual finger vein images and produced very satisfactory reassembly results.     

Image restoration of skin scattering and optical blurring for finger vein recognition

Finger vein recognition has been adopted due to its high recognition rate and the invisibility of vein in visible light. However, because a finger vein pattern is not distinctive due to light scattering in the skin layer, the localization of a finger vein pattern region using an image processing algorithm is a difficult procedure. Also, optical blurring increases these difficulties. We propose a new finger vein image restoration method to deal with skin scattering and optical blurring. Our research is novel in three ways compared to previous studies. Firstly, the amount of optical blurring of a finger vein image is measured based on the average gradient of the orthogonal profile of a finger edge. Secondly, the accurate point spread function (PSF) of optical blurring is adaptively determined based on the orthogonal profile of a finger edge. Thirdly, using a constrained least square (CLS) filter and optimized parameters in terms of the lowest error of finger vein recognition, the restoration of a skin scattered and optically blurred finger vein pattern was performed. Experimental results show that the equal error rate (EER) of finger vein recognition with restoration was reduced by as much as 3.14% (5.05-1.91%) compared to the EER without restoration.     

Anti-spoof touchless 3D fingerprint recognition system using single shot fringe projection and biospeckle analysis

Fingerprint is a unique, un-alterable and easily collected biometric of a human being. Although it is a 3D biological characteristic, traditional methods are designed to provide only a 2D image. This touch based mapping of 3D shape to 2D image losses information and leads to nonlinear distortions. Moreover, as only topographic details are captured, conventional systems are potentially vulnerable to spoofing materials (e.g. artificial fingers, dead fingers, false prints, etc.). In this work, we demonstrate an anti-spoof touchless 3D fingerprint detection system using a combination of single shot fringe projection and biospeckle analysis. For fingerprint detection using fringe projection, light from a low power LED source illuminates a finger through a sinusoidal grating. The fringe pattern modulated because of features on the fingertip is captured using a CCD camera. Fourier transform method based frequency filtering is used for the reconstruction of 3D fingerprint from the captured fringe pattern. In the next step, for spoof detection using biospeckle analysis a visuo-numeric algorithm based on modified structural function and non-normalized histogram is proposed. High activity biospeckle patterns are generated because of interaction of collimated laser light with internal fluid flow of the real finger sample. This activity reduces abruptly in case of layered fake prints, and is almost absent in dead or fake fingers. Furthermore, the proposed setup is fast, low-cost, involves non-mechanical scanning and is highly stable.     

Multimodal biometric authentication based on score level fusion of finger biometrics

In this article, we propose a novel finger multimodal biometric authentication that combines finger vein, fingerprint, finger shape and finger knuckle print features of a single human finger. The proposed multimodal biometrics provides score-level fusion approach based on triangular norm with four finger biometric traits, instead of two or three ones combined in the previous approaches. The experimental evaluations and analysis are conducted on a merged multimodal biometrics database. The results show that the proposed score-level fusion approach using triangular norm obtains a larger distance between genuine and imposter score distribution as well as achieves lower error rates. Moreover, the comparison results suggest that the proposed score level fusion of finger biometrics using triangular norm outperforms the state-of-the-art approaches.     

基于自适应下采样的移动机器人视觉定位技术

视觉定位是移动机器人定位的一个主要发展方向,针对传统视觉定位技术实时性差的问题,提出一种基于自适应下采样的快速视觉定位技术。通过预先得到的尺寸特征,并根据最小分辨尺寸计算下采样率,而后对下一副图像进行下采样及图像分割,根据对象坐标和下采样率确定对象在源图像中所处区域,对源图像该区域进行图像分割和特征提取。将提取的尺寸特征作为下一幅图像的输入,提取的视觉定位所需特征用于机器人的定位解算,由此在保证视觉定位精度的前提下有效减少视觉定位的时间。实验表明,对文中给定图像,传统方法处理100幅图像时间为20.23 s,而文中所述技术对应图像处理时间为1.78 s,仅为传统技术的8.8%,有效减少了图像处理时间,提高了机器人视觉定位的实时性。     

Impact of NIR wavelength lighting in image acquisition on finger vein biometric system effectiveness

Finger vein biometric systems become increasingly more popular because they offer higher security comparing to other authentication solutions with respect to positive persons experience. Those systems operate on near infrared light (NIR) in wavelength range from around 700 to 1000 nm, however dedicated research to determine impact of NIR lighting on biometric system effectiveness has not been conducted and presented in the literature ever before. In this paper the study of correlation between wavelengths in NIR spectra and effectiveness of person identification in a biometric system is presented. To achieve that goal, a new model of image acquisition system allowing change of light wavelengths has been created and NIR finger vein dataset containing 11 556 images was established. Furthermore, this model was used to perform experimental work and proof that some NIR wavelengths better suit for vein patterns acquisition, allowing to increase the recognition effectiveness of finger vein biometric systems.     

基于局部线性变换的色阶映射算法

为了提高高动态图像显示的视觉效果,提出了一种基于局部线性变换的色阶映射算法。针对图像直接线性压缩能力较差的缺点,本文提出一种局部的线性变换模型。由于局部窗的结构,是利用局部窗的中心像素点与窗内的其他像素点的均值,方差的关系构造而成,因此基于局部线性变换的色阶映射算法能够抑制由具有高对比度的图像边缘所引起的鬼影和光晕现象。首先,根据线性变换将图像的色阶映射转换为求解泛函的最优解;其次,通过推导求解出线性变换的参数并将泛函最优化问题转换为求解一个线性方程组问题;最后求解线性方程组,得出视觉效果较好的低动态范围图像。有效避免传统色阶映射出现的光晕和亮度不连续的现象,能够更多的保留高动态图像的细节。最后通过实验结果对比分析,通过信息熵和对比度两个参数可以体现文中算法较好。     

矿山多源遥感图像像点位移测试系统设计

针对矿山地形的起伏变化影响对其进行分析研究的准确性,提出矿上多源遥感图像像点位移测试系统的设计方法。结合GPS定位原理,选取遥感图像上某一时刻点作为测试点,对拍摄得到的矿山图像和电子测试系统各触点之间的距离进行计算,进而得到测试点具体坐标位置,将其与测试点真实现场坐标相对比,实现矿山多源遥感图像像点位移的测试。通过实验证明,所设计的测试系统可以高精度实现对遥感图像像点位移的测试,有效减小了矿山地形变化带来的遥感图像研究误差。     

基于物联网的人工智能图像检测系统设计与实现

物联网络的建立促使人工智能领域取得飞跃性进展。传统图像检测方法利用小波能算法进行背景与边缘噪声划分方式进行图像检测,存在低分辨率图像检测精度低、检测速度慢、缺乏图像深度分析等一系列问题。物联网人工智能发展迅速的环境下,提出基于物联网的人工智能图像检测系统设计。采用智能人工像素点特征采集技术(IAPCCT),对图像进行逐点特征提取,运用物联网丰富数据量资源与处理运算能力对采集图像像素点进行特征分析回馈,回馈信号经人工智能信号图像合成模块(AISIS),对信号做图像转换处理并输出分析结果完成图像检测。通过仿真实验测试证明,基于物联网的人工智能图像检测系统设计具有图像检测率高、识别准确度高、运行稳定、处理高效等优点。     

Hand vein recognition based on the connection lines of reference point and feature point

According to the essential characters of the image topology, a new hand vein recognition algorithm based on the connection lines of reference point and feature points is proposed. In this method, the intersection points and the endpoints of the vein image are used as feature points. After the intersection points and the endpoints selected as feature points, the reference point for image matching are extracted from these points. The relative distances between the reference point and the feature points and the angles between the adjacent connections of the reference point and feature points are calculated and used as recognition features. Finally these two features are combined for hand vein recognition. This method can effectively overcome the influence on the recognition results caused by image translation and rotation. Experimental results show that the proposed algorithm is able to achieve hand vein recognition reliably and quickly.