matlab.doc

实现图像的滤波、锐化、边缘检测、微分处理及图像平滑(Image filtering, sharpening, edge detection, differential processing and image smoothing)

一个非常实用的las文件处理工具，可以实现很多功能，如读写las文件，分类，分块，抽稀，合并，生成边界线，生成DEM，点云高度归一化，数据格式转换，波形数据压缩等(A very useful the las file processing tools, you can achieve a lot of features, such as read and write las file classification block Rarefy merge to generate the boundary line, generated DEM point cloud height normalized data format conversion, waveform data compression, etc.)

VC摄像图捕捉程序，简单实用，对初学者有帮助(VC camera Figure capture, simple and practical for beginners)

人脸检测与跟踪，我的毕业论文，使用我自己改进的ADABOOST和CAMSHIFT算法,附带我的毕业论文,opencv1.0或以上自己配置(face detection use adaboost and camshift algorithm，relevant paper enclosed，OPENCV1.0 is necessary)

特征点匹配时，采用构造KD-tree，用最优节点优先法寻找最佳匹配点(Matching feature points, the tectonic KD-tree, with the optimal node is preferred method to search for the optimal matching point)

说明：  基于LBP纹理特征的运动目标检测算法.rar(Moving target detection algorithm based on LBP texture features. Rar)

IMM交互式对机动目标跟踪，程序写的很完美，包含各种常见的滤波算法(IMM UKF KF)

基于VC环境下的直线裁剪算法Cohen-Sutherland算法和Liang-Barsky算法的实现(Based on the realization of linear cutting algorithm VC environment under the Cohen-Sutherland algorithm and Liang-Barsky algorithm)

：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)

机器视觉,计算机视觉,人脸识别,形态学,图像采集,压缩编码,数字水印,神经网络,人工智能,模式识别,特征提取,图像检索,视频检索,计算机图形学(machine vision, computer vision, face recognition, morphology, image acquisition, compression, digital watermarking, neural network, artificial intelligence, pattern recognition, feature extraction, image retrieval, video retrieval, computer graphics)