基于Hilbert曲线和位置自调节法的图像版权保护算法

以小波的相关理论为基础,先把水印图像预置乱处理,对宿主图像直接进行小波包分解,之后将水印的低频部分和中高频部分分别采用Hilbert曲线扫描和位置自调节法嵌入到宿主图像中,嵌入强度可根据图像的内容自适应调节。通过实验观察,新算法在抗压缩和抗剪切性能有所提升,同时隐蔽性和抗噪能力也得到了较大改观,结果充分说明该算法是一种性能较为优良的图像版权保护算法。     

基于退化四元小波变换的纸币识别

针对如何提取纸币图像特征和提高识别率的问题,综合利用退化四元小波变换具有的相位特性,提出一种基于退化四元小波变换的纸币识别方法.该方法首先对采集的纸币图像进行倾斜校正和边缘检测,然后运用退化四元小波对纸币图像进行分解操作,并对分解系数进行统计分析,将每个分解子带系数的能量和标准差作为该纸币图像的特征向量,最后将支持向量机作为分类器对纸币图像进行识别.本文方法在资源约束的嵌入式清分系统上实现,实验结果表明采用本文提出的算法突破了传统纸币识别系统识别率很难再提高的瓶颈,同时能够满足清分系统的实时性要求.     

基于集成温度传感器的硅微陀螺仪数字化温度补偿研究

提出了一种基于集成温度传感器的硅微陀螺仪数字化温度补偿方法。首先,介绍了集成温度传感器的硅微陀螺仪基本结构原理,分析了硅微陀螺仪动力学方程以及温度变化对硅微陀螺仪谐振频率、品质因数、标度因数和零偏的影响。然后,设计了基于FPGA的硅微陀螺仪数字化补偿电路。最后,经过温度特性实验得到标度因数和零偏随温度变化曲线,建立了温度补偿模型,提出分段温度补偿方法。经过温度补偿后,标度因数和零偏的温度系数分别由316.66×10-6/℃和366.22°/(h·℃)减小为69.67×10-6/℃和115.25°/(h·℃),证明了补偿方法的正确性和可行性。     

基于级联迭代傅里叶变换算法的光学安全技术

基于级联迭代傅里叶（CIFT）变换算法提出了一种新的光学图像的加密方法,该方法通过输入和4-F相关的傅里叶域的模糊设计和定位,从而实现光学加密以及真实性验证。与以往的方法有所不同,新算法采用改进的搜索策略：修改两个阶段的同步分布,同时扩大搜索空间。计算机模拟实验表明其快速的收敛算法能够更好地恢复图像的质量,密钥会被分配到不同的方面,用户只有在得到这些密钥的授权之后才能得到解密后的图像,这种密钥分配策略可以极大减少被入侵的风险。     

Contourlet变换与生命游戏置乱相结合的视频零水印

为了达到版权保护的目的,提出一种Contourlet变换与生命游戏置乱相结合的视频零水印,Contourlet被誉为处理二维信号的最优解,算法利用其优势对关键帧处理得到低频系数,再经过核心算法处理,最后结合生命游戏规则生成特征图像,并运用加密算法对水印进行预处理以加强安全性。实验结果表明本算法较好地折中了数字水印的不可见性和鲁棒性,并能有效地抵抗常见的几何攻击和视频攻击。     

基于数据分布的聚类联邦学习

联邦学习(federated learning)可以解决分布式机器学习中基于隐私保护的数据碎片化和数据隔离问题。在联邦学习系统中,各参与者节点合作训练模型,利用本地数据训练局部模型,并将训练好的局部模型上传到服务器节点进行聚合。在真实的应用环境中,各节点之间的数据分布往往具有很大差异,导致联邦学习模型精确度较低。为了解决非独立同分布数据对模型精确度的影响,利用不同节点之间数据分布的相似性,提出了一个聚类联邦学习框架。在Synthetic、CIFAR-10和FEMNIST标准数据集上进行了广泛实验。与其他联邦学习方法相比,基于数据分布的聚类联邦学习对模型的准确率有较大提升,且所需的计算量也更少。     

Discriminative multi-task objects tracking with active feature selection and drift correction

In this paper, we propose a discriminative multi-task objects tracking method with active feature selection and drift correction. The developed method formulates object tracking in a particle filter framework as multi-Task discriminative tracking. As opposed to generative methods that handle particles separately, the proposed method learns the representation of all the particles jointly and the corresponding coefficients are similar. The tracking algorithm starts from the active feature selection scheme, which adaptively chooses suitable number of discriminative features from the tracked target and background in the dynamic environment. Based on the selected feature space, the discriminative dictionary is constructed and updated dynamically. Only a few of them are used to represent all the particles at each frame. In other words, all the particles share the same dictionary templates and their representations are obtained jointly by discriminative multi-task learning. The particle that has the highest similarity with the dictionary templates is selected as the next tracked target state. This jointly sparsity and discriminative learning can exploit the relationship between particles and improve tracking performance. To alleviate the visual drift problem encountered in object tracking, a two-stage particle filtering algorithm is proposed to complete drift correction and exploit both the ground truth information of the first frame and observations obtained online from the current frame. Experimental evaluations on challenging sequences demonstrate the effectiveness, accuracy and robustness of the proposed tracker in comparison with state-of-the-art algorithms.     

Multi-resolution feature fusion for face recognition

For face recognition, image features are first extracted and then matched to those features in a gallery set. The amount of information and the effectiveness of the features used will determine the recognition performance. In this paper, we propose a novel face recognition approach using information about face images at higher and lower resolutions so as to enhance the information content of the features that are extracted and combined at different resolutions. As the features from different resolutions should closely correlate with each other, we employ the cascaded generalized canonical correlation analysis (GCCA) to fuse the information to form a single feature vector for face recognition. To improve the performance and efficiency, we also employ “Gabor-feature hallucination”, which predicts the high-resolution (HR) Gabor features from the Gabor features of a face image directly by local linear regression. We also extend the algorithm to low-resolution (LR) face recognition, in which the medium-resolution (MR) and HR Gabor features of a LR input image are estimated directly. The LR Gabor features and the predicted MR and HR Gabor features are then fused using GCCA for LR face recognition. Our algorithm can avoid having to perform the interpolation/super-resolution of face images and having to extract HR Gabor features. Experimental results show that the proposed methods have a superior recognition rate and are more efficient than traditional methods.