compressed-sensing

短时傅里叶变换,主要用于故障诊断和信号处理，很好用(Short-time Fourier transform)

运动估计的几种方法，包括线性预测法、混合高斯法、特征值法和相邻帧差法等(Several motion estimation methods, including linear prediction method, mixed-Gaussian law, Eigenvalue and adjacent frames, such as poor law)

编程实现了抑制sar图像相干斑噪声的精致lee滤波算法，效果很好(refined lee)

python写的图像相似度算法。比较图像相似度，选出最相思的图片(python write the image similarity algorithm. Compare image similarity, image to select the most Acacia)

说明：  模式识别与图像处理，图像的边缘处理，增强(Pattern recognition and image processing, image edge processing, to enhance)

在本文件中，我主要研究通过行人的表观特征进行识别，例如颜色、纹理等等。用matlab GUI 编写了程序，对行人进行匹配 (Pedestrian recognition of the inter-camera is intelligent surveillance camera network a key research direction. Match the pedestrian different camera, you can cross the pedestrian track of the camera, the expansion of the monitoring horizon, at the same time can have a clearer grasp of the behavior of pedestrians in the entire surveillance area. Different cameras, different lighting conditions, the pedestrian position and a different scale, shelter, etc., making the pedestrian match challenging. In this document, I study to identify by pedestrian apparent characteristics such as color, texture, and so on.)

毕业设计，基于内容的图像检索，支持的检索特征包括 sift,颜色直方图，灰度矩阵，HU不变矩，边缘方向直方图，检索方法使用K-means和K-D树两种，需要OPENCV支持，运行时请先选定一个文件夹来生成特征库，特征库用access数据库保存，只支持JPG文件(Graduate design, content-based image retrieval, search features, including support sift, color histogram, gray matrix, HU moment invariants, edge direction histogram, retrieval method using the K-means and KD trees are two kinds of needs OPENCV support Please select a runtime folder to generate the feature library, feature library with access database save, only supports JPG files)

介绍混合像元解混里面的端元提取算法N-FINDR(Describes the mixed pixel unmixing inside endmember extraction algorithm N-FINDR)

This extracts features images, that can be used to detect humans and objects.

说明：  有数字1和2的图片各四十张，利用mat lab读取图片并将其二值化，得到只由0和1表示的矩阵，根据数字的不同特征，选择两个特征值，用来对分类器进行训练，最后用剩下的5个1和5个2来进行测试，看分类器是否可以正确的对数据进行分类。(Figures 1 and 2 pictures of the 40, using mat lab picture and secondly, the value of reading, and received only by 0 and 1, said matrix, according to the number of different features, select the two characteristic values, used to classifier training, and finally with the remaining 5 1 and 5 2 to carry out tests to see whether the classifier can correctly classify the data.)