1111222223333334444445533332345554

数字图像处理实验——直方图的均衡化，打开图像并实现其均衡化(Digital image processing experiments- histogram equalization, open the image and realize its equalization)

图片处理 去噪音，去模糊为了实现较好保留图像边缘特征的有效去噪,提出了一种基于边缘检测的Contourlet 变换去噪方法(Picture Deal)

说明：  解决一些图像处理的小问题，具体的划分和问题，文件里有提到(Solve some small problems in image processing)

说明：  基于边缘特征的SAR图像与光学图像匹配，测度为Hausdorff距离(SAR images based on edge features and optical image matching, for the Hausdorff distance measure )

demons三维非刚性图像配准，速度很快，基于ITK(demons of 3D non-rigid image registration, very fast, based on ITK)

基于非局部图像去噪模型的遥感图像融合的变分方法。该变分方法采用梯度下降法。(Variational method of remote sensing images nonlocal image denoising model fusion. The variational method using gradient descent.)

硕士学位论文:在高分辨率成像声纳系统和回波测深仪的两个控制平台的基础上对声纳图像数据的高速显示技术的研究。文中所有的程序编写都是使用C和C++语言，在WindowsXP平台下利用VC++6．0进行编译和调试的。(study for high speed display technology on sonar image data)

LSB替换隐写软件，该软件能把ASCII码嵌入到图像中，其载体图像是24位的BMP格式图像(LSB Steganography tool which provides hide ASCII infomation within 24-bit Bitmap images)

通过两台相机拍摄的立体像对完成对目标的快速实时测量，用于构建道路交通等数据快速采集系统。(Through two three-dimensional camera for the completion of the goal as fast real-time measurement, used to build road traffic, such as rapid data acquisition system.)

：植物种类识别方法主要是根据叶片低维特征进行自动化鉴定。然而，低维特征不能全面描述叶片信息，识别准确率低，本文提 出一种基于多特征降维的植物叶片识别方法。首先通过数字图像处理技术对植物叶片彩色样本图像进行预处理，获得去除颜色、虫洞、 叶柄和背景的叶片二值图像、灰度图像和纹理图像。然后对二值图像提取几何特征和结构特征，对灰度图像提取 Hu不变矩特征、灰 度共生矩阵特征、局部二值模式特征和 Gabor 特征，对纹理图像提取分形维数，共得到 2183 维特征参数。再采用主成分分析与线性 评判分析相结合的方法对叶片多特征进行特征降维，将叶片高维特征数据降到低维空间。使用降维后的训练样本特征数据对支持向量 机分类器进行训练(plant species identification method is mainly based on blade automatic identification of low dimensional characteristics.However, can not fully describe blade low-dimensional feature information, identification accuracy is low, in this paper A kind of plant leaves recognition method based on multiple feature dimension reduction.First by digital image processing technology to the plant leaf color sample image preprocessing, obtain background color removal, wormhole, petioles, and the blades of a binary image, gray image and texture image.Then the binary image to extract the geometric characteristics and characteristics of structure and characteristics of gray image extraction Hu moment invariants, gray co-occurrence matrix feature, local binary pattern features and Gabor, to extract the fractal dimension of texture image, get 2183 d characteristic parameters.By principal component analysis and linear uation analysis method of combining the characteristics of blade more feature dimensi)