Matlab-bingtu

基于PCA的雷达点云数据的形状特征提取,主要是屋顶平面提取(The feature extraction of LiDAR point cloud data based on PCA)

基于MATLAB的图像处理源程序集，包括阈值、二值化、灰度变化，边缘提取等等(MATLAB based image processing source code set, including threshold, binary, gray scale, edge extraction, etc.)

此算法为基于k-means聚类的非局部均值快速去噪算法，方法便捷有效，可保持边缘信息(this is a fast edge-preserving denoising method based on non-local means method with k-means clustering)

meanshift算法的python实现 + 图像化显示(meanshift with python)

说明：  图像特征提取的Contourlet变换工具包，可以设置分解层次，例如，三层分解提取17维特征，用于纹理图像或者SAR图像分割应用中(Image feature extraction toolbox of Contourlet transform, you can set the level of decomposition, for example, three level with 17-dimensional, for the segmentation on texture image or SAR Image)

基于dft的数字水印算法，希望对大家有用(this is good)

该程序实现了相位编组法检测，可以将灰度缓慢变化的边缘提取出来(Realize the process of phase formation assay can be gray-scale changes in the edge of the slow extracted)

说明：  LSA的第一步是要去创建词到标题（文档）的矩阵。在这个矩阵里，每一个索引词占据了一行，每一个标题占据一列。每一个单元（cell）包含了这个词出现在那个标题中的次数。例如，词”book”出现在T3中一次，出现在T4中一次，而”investing”在所有标题中都出现了一次。一般来说，在LSA中的矩阵会非常大而且会非常稀疏（大部分的单元都是0）。这是因为每个标题或者文档一般只包含所有词汇的一小部分。更复杂的LSA算法会利用这种稀疏性去改善空间和时间复杂度。(The Little Book of Common SenseInvesting: The Only Way to Guarantee Your Fair Share of StockMarket Returns ,)

说明：  用水平集方法进行细胞图像的分割大家可以看看写的有点简单(level set method using cell image segmentation we can see a little bit simpler to write)

说明：  本课题的主要内容是图像预处理,它主要从摄像头中获取人脸图像然后进行处理,以便提高定位和识别的准确率.该模块主要包含光线补偿、图像灰度化、高斯平滑、均衡直方图、图像对比度增强，图像预处理模块在整个系统中起着极其关键的作用，图像处理的好坏直接影响着后面的定位和识别工作,内有源代码和全部论文资料(this issue is the major content of image preprocessing, mainly from the camera to obtain images Face then, in order to improve the recognition and positioning accuracy. The module consists mainly of light compensation, Grayhound, Gaussian smoothing, balanced histogram, image contrast enhancement, image pre- processing module in the system plays a crucial role in image processing will have a direct impact behind the positioning and identification, within Active code and all papers information)