rgb2hav

该程序是发表在IEEE上的经典直线提取论文“Extracting Straight Lines”的源代码实现，对计算机视觉、模式识别和信息提取等领域的科学研究人员具有极其重要的参看意义。建议下载学习和适用。(The program is published in the IEEE paper on the classic straight-line extraction " Extracting Straight Lines" the source code implementation of computer vision, pattern recognition and information extraction and other areas of scientific researchers see an extremely important significance. Recommended to download to learn and to apply.)

读取dxf文件，并利用opencv画出2D形状(Read the DXF file, and draw a 2D shape using OpenCV)

esprit算法对有干扰的信号频率进行估计,一种基于多文档得图像合并技术，搭建OFDM通信系统的框架。( esprit algorithm signal frequency interference can be assessed Based on multi-document image obtained combining technique, Build a framework OFDM communication system.)

这些代码是有关数字图像处理的一些算法，大家有需要的，可以在里面找些与自己相关的(These codes are some of the digital image processing algorithms, we need, in which you can find some with their associated)

简单的图形文字识别软件，希望大家喜欢我的分享，谢谢(Simple graphical text recognition software, I hope you like my share, thank you)

基于稀疏表示的图像融合算法 KSVD OMP(Image fusion algorithm based on sparse representation KSVD OMP)

说明：  利用已知的平坦道路的视差图，得出uv方向的直方图，并且求出直线(The histogram of the UV direction and the straight line are obtained by using the disparity map of the known flat road.)

说明：  本程序在EVC4.0+PPC SDK 2003下调试通过。用Independent JPEG Group发行的JpegLib进行Jpeg图像的读取与保存。(This procedure in EVC4.0+ PPC SDK 2003 adopted under the debugger. By Independent JPEG Group issued JpegLib to read Jpeg images and preservation.)

中科院图像理解与计算机视觉课件6 值得参考一下 some topics on pattern recognition and computer vision(Chinese Academy of Sciences and Computer Vision Image Understanding Courseware 6 worth refer to some topics on pattern recognition and computer vision)

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. )