GMM-modeling-and-EM

96个蛮实用的PNG格式图标 仅供编程或图像处理使用。(96 pretty practical PNG format icon super practical!)

opencv图像库函数集，有关最新的opencv新增函数的应用(opencv functions)

数字图像处理的基础知识学习 涵盖源代码，有助于提高编程(digital image processing )

实现对鱼眼图像的校正 我自己执行过 可以使用(it can realise the rectification of the fish eye image )

图像处理中的数字识别代码，包括数字训练集合和训练分类算法，可以实际应用。(digital recognition)

数字图像处理实验——直方图的均衡化，打开图像并实现其均衡化(Digital image processing experiments- histogram equalization, open the image and realize its equalization)

说明：  基于LBP纹理特征的运动目标检测算法.rar(Moving target detection algorithm based on LBP texture features. Rar)

数字水印处理，嵌入、提取、认证，可以在图形，视频和文本中添加数字水印，增强文档的保密，防止泄密(Digital watermarking processing, embedding, extraction, authentication, can be in graphics, video and digital watermarking to add text to enhance the confidentiality of documents to prevent leak)

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. )

说明：  用canny算子来检测图像边缘，VC++(Using canny operator to detect image edge)