MatlabBitPlaneDecomposition

Image Watershed Segmentation图像边缘提取(Image Watershed Segmentation Image Edge Detection)

图像从背景图中提取 可以用来提取边缘，将背景图与图像分离(Images extracted from the background image can be used to extract the edge of the background map and image separation)

说明：  编程实现小波的边缘检测算法，对图片的边缘处理(Edge Detection Algorithms of Wavelet Based on Programming)

光流分析程序 利用光流分析进行运动目标检测程序 (Optical flow analysis program using optical flow analysis of moving target detection procedures)

IDL中用于读取ENVI格式的遥感卫星影像数据的IDL程序(IDL, ENVI format for reading remote sensing image data on the IDL)

avi视频提取出书帧图像，然后对图像进行处理，经过处理后的图像帧再写成另一段的avi视频。(avi video video frame extracted, processed image frame, and then the images will be referred to the district into another frame of video avi.)

说明：  使用gpu、cpu并行进行sift算子计算匹配，能够在原来的基础上加速处理，但对显卡要求较高，具体环境配置使用方法可以参照mannual(SiftGPU is an implementation of SIFT [1] for GPU. SiftGPU processes pixels parallely to build Gaussian pyramids and detect DoG Keypoints. Based on GPU list generation[3], SiftGPU then uses a GPU/CPU mixed method to efficiently build compact keypoint lists. Finally keypoints are processed parallely to get their orientations and descriptors. SiftGPU is inspired by Andrea Vedaldi s sift++[2] and Sudipta N Sinha et al s GPU-SIFT[4] . Many parameters of sift++ ( for example, number of octaves, number of DOG levels, edge threshold, etc) are also available in SiftGPU. The shader programs are dynamically generated according to the parameters that user specified. SiftGPU also includes a GPU exhaustive/guided sift matcher SiftMatchGPU. It basically multiplies the descriptor matrix on GPU and find closest feature matches on GPU. Both GLSL and CUDA implementations are provided. )

利用基于直方图的自适应阈值方法实现分割前景与背景(Segmentation of foreground and background using histogram-based adaptive threshold method)

这个是一个非常好的社区发现算法，该算法社区发现的精度非常高。(this is a algorihtm about community detection)

说明：  基于小波变换的图像增强代码，试过了，很好用，希望对大家有帮助。。。。(Image enhancement based on wavelet transform code, tried, very good use, we want to help. . . .)