hyperIcaEea

采用haar小波实现二维图像的分解与重构（mallat算法）：(Two-dimensional image using haar wavelet decomposition and reconstruction (mallat algorithm):)

使用matlab调用opencv做成的adaboost人脸检测DLL模块，在usb摄像头采集的视频序列中检测人脸并实时显示， 实时性比C低。 下载后可以直接在matlab运行，不要改动文件夹内的文件相对位置。(Opencv matlab call made using adaboost face detection DLL module, usb camera capture video sequences of face detection and real-time display, real-time lower than C. Download can be run directly in matlab, do not change the file folder relative position.)

通过erdas遥感软件提取水体的三种操作(Extracting water through software operation of the three operating)

实现多层tiff显微图像识别，追踪显微镜下PS微球的坐标并给出颗粒运动轨迹(Multi-layer TIFF micro-image recognition is realized. The coordinates of PS microspheres under the microscope are tracked and the trajectories of particles are given.)

基于像素标记的连通区域计算程序，只针对二值图像，随附有。(Based on regional connectivity calculation procedure pixel tags, only for the binary image, comes with.)

基于序贯蒙特卡罗方法的人体跟踪程序，对于学习蒙特卡罗粒子滤波器的人有重要参考价值。(Sequential Monte Carlo method based on human tracking procedures, Monte Carlo particle filter for learning a person has an important reference value.)

基于亚像素运动估计算法的块匹配运动，有源码跟参考文献，初学的小伙伴可以看看(Block-matching motion estimation algorithm based sub-pixel motion, there is source code with references, beginner can look at a small partner)

VapourSynth-BM3D-master BM3D算法的原创代码，运行速度超快，参考文献《Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering》(VapourSynth-BM3D-master BM3D original algorithm code, run super-fast, Reference " Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering" )

该算法首先通过字典学习得到含噪图像的冗余字典，然后对相似的图像块进行聚类构成块群，并通过迭代收缩和L1正则化约束，对同类的图像块在字典上进行稀疏表示，以达到降噪的目的。实验结果表明，在常规的图像处理上，本文提出的算法能较好的保留图像的结构信息，与K-SVD和BM3D等现有的流行算法相比，具有更高的峰值信噪比（PSNR）(It firstly get the redundant dictionary of a noised image by dictionary learning.Then,the image patches are gathered according to their similarities.Meanwhile,the similar patches get sparse representation showed in dictionaries by iterative shrinkage and L1 regularization constraints and eventually the image is restored and noise is removed.The experimental results indicate that the proposed algorithm can well preserve the structure information of the common image with a higher Peak Signal to Noise Ratio(PNSR),compared with state-of-the-art algorithms,such as K-SVD and BM3D)

彩色图像处理 格式转化rgb2hsi，hsi2rgb 冈萨雷斯(AN M-function fro converting from RGB TO HSI, HSI to RGB)