MATLAB

document about computer vision - image processing

说明：  本代码可以载入超声图像，然后通过标注连通区域，自动截取最大连通区域对应的图像部分，达到去除四周标注的目的(After loading the ultrasonic image, the image part can be automatically cut off to remove the surrounding annotation)

实现了三种图像匹配算法 1：归一化互相关匹配算法 2：基于Hausdorff距离的图像匹配算法 3：图像不变矩匹配算法(Implements three image matching algorithm 1: normalized cross correlation matching algorithm 2: image matching algorithm based on Hausdorff distance 3: The image matching algorithm invariant moments)

说明：  MATLAB转化大地坐标与球坐标之间的转化，，其中包含了空间直角坐标系与大地坐标系的转化(Matlab transformation of geodetic and spherical coordinates)

图像配准，利用互相关的方法进行图像配准，能达到良好效果(Image registration, the use of the cross-correlation method of image registration, and can achieve good results)

是图像处理中常用的测试图库，非常实用,如lena图片， 青椒图片，以及zelda图片等(is the test pictures used in image processing very useful)

Fast_and_Robust_Multi-Frame_Super-Resolution这篇文章的算法实现。内含BTV算法和改进的btv算法实现超分辨率重建。(This article Fast_and_Robust_Multi-Frame_Super-Resolution algorithm. Containing BTV algorithm and improved the btv algorithm to achieve super-resolution reconstruction.)

光流法实时实现的文献及代码，2012发表于cvpr，200*200的图像，每秒可达200帧，2012_CVPR_Lip-motion events analysis and lip segmentation using optical flow(Optical flow method to achieve real-time literature and code, 2012 published in cvpr, 200* 200 images per second, up to 200, 2012_CVPR_Lip-motion events analysis and lip segmentation using optical flow)

说明：  一个图像变换程序，检测人脸，并剪切出需要的部分(An image transformation program, change the pixel size, cut out part of the required)

：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)