mojidazhichongjian

实现纹理模式的LBP特征表示及分类。 实现一种基于局部二值模式LBP（Local Binary Pattern）的多分辨率灰度尺度及旋转不变性的纹理分类方法(LBP texture model to achieve that as well as the breakdown characteristics. The realization of a model based on local binary LBP (Local Binary Pattern) Multiresolution gray-scale and rotation invariant texture classification)

用CPHD的方法实现多目标跟踪，及对CPHD性能的检测。(With CPHD method to achieve multi-target tracking, and performance testing for CPHD.)

图论分割算法之一“Nystrom Ncut”MatLAB源程序。(MatLAB codeof “Nystrom Ncut”.)

Hermite-Gaussian光束是一种独特的光束，可以作为图形处理中变化的基函数(The Hermite-Gaussian beam is a unique light beam can be used as the base function of the change in the graphics processing)

经典的图像分割算法ostu,去两峰值之间的峰谷为阀值(Classical image segmentation algorithms ostu, to the peak and valley between two peaks as the threshold)

关于图像配准的经典外文文献，影响因子很高(on the registration of foreign classic literature, high impact factor)

VS 环境下利用opencv 实现图像的傅里叶变换，输入图片，保存输出结果。(Under VS environment use opencv to achieve image Fourier transform, the input image and save the output.)

LSB算法实现BMP图片信息的隐藏和提取 语言：C#(LSB algorithm BMP image information hiding and extraction languages: C#)

：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)

基于贝叶斯最小错误率算法的崇明岛遥感图像分类程序。并且有饼状图显示各类地物比例(Minimum error rate based on Bayesian algorithm for Chongming Island, the remote sensing image classification procedures. And the pie chart below shows the proportion of various types of features)