一种基于小波变换的照明无关边缘检测和模糊增强方法

提出了一种基于小波变换的照明无关边缘检测和模糊增强算法,用于从不均匀的弱照明图像中提取目标边缘。依据照明反射图像形成模板和CCD相机成像公式,推导出图像的小波变换公式。对图像局部区域中边缘与背景像素的小波系数进行比较分析,设计了一种照明无关的小波边缘检测公式。给出一种同时考虑小波模值大小和梯度方向的模糊算子来增强边缘并抑制噪声。最后,采用仿真和真实的图像对该算法进行验证,利用此算法检测阶梯边缘,得到该算法的边缘检测评价标准F系数值为0.984 3,边缘定位精度评价系数E_d值为0.126 5,通过被检测的特征球边缘计算得到的交比值误差为3.72×10^-3。实验结果证实,该边缘检测方法能够很好地工作于非均匀的弱照明图像。     

抗外点干扰的鲁棒AdaBoost分类器构建方法

根据AdaBoost算法易受外点影响这一缺陷,提出一种利用Ransac算法实现抗外点干扰的鲁棒AdaBoost分类器构建方法。不同于其他AdaBoost算法在分类器构建中单纯使用样本加权或权值控制的手段,该算法将Ransac算法引入AdaBoost分类器模型构建过程中,去除潜在外点,克服现有AdaBoost算法缺陷。同时,借助Ransac算法,从全部AdaBoost分类器中选择最佳分类器模型,消除由外点引起的分类器降级。最后,将该AdaBoost分类器模型用于含有一定量外点的笔迹样本进行验证,实验结果证明了该方法的有效性。     

自适应和动态的手机数据加密算法（英文）

To enhance the security of user data in the clouds,we present an adaptive and dynamic data encryption method to encrypt user data in the mobile phone before it is uploaded.Firstly,the adopted data encryption algorithm is not static and uniform.For each encryption,this algorithm is adaptively and dynamically selected from the algorithm set in the mobile phone encryption system.From the mobile phone's character,the detail encryption algorithm selection strategy is confirmed based on the user's mobile phone hardware information,personalization information and a pseudo-random number.Secondly,the data is rearranged with a randomly selected start position in the data before being encrypted.The start position's randomness makes the mobile phone data encryption safer.Thirdly,the rearranged data is encrypted by the selected algorithm and generated key.Finally,the analysis shows this method possesses the higher security because the more dynamics and randomness are adaptively added into the encryption process.