ImageRetrieval

图像的纹理分析应用实例,主要可用于模式识别当中(image texture analysis application, which can be used for pattern recognition)

一个非常实用的las文件处理工具，可以实现很多功能，如读写las文件，分类，分块，抽稀，合并，生成边界线，生成DEM，点云高度归一化，数据格式转换，波形数据压缩等(A very useful the las file processing tools, you can achieve a lot of features, such as read and write las file classification block Rarefy merge to generate the boundary line, generated DEM point cloud height normalized data format conversion, waveform data compression, etc.)

打开bmp位图文件 彩色转灰阶 直方图均衡 直方图规定 灰阶线性化(Open the bmp grayscale bitmap image binarization functions connected domain extraction features)

处理Gamma乘性噪声的PPB方法，考虑了非局部patch的相似性，可处理SAR图像去噪。(The PPB method for multicative noise removal, which based on the Probabilistic Patch similarity.)

数字图像处理 opencv 一些基本例子和源码(opencv code)

本书系统地介绍了图像处理与识别的基本原理、典型方法和实用技术。全书共分12章，第1章～第6章是图像处理与识别的基础内容，包括图像科学综述、MATLAB语言图像编程、图像增强、图像分割、图像特征提取和图像识别；第7章～第10章是图像处理与识别的工程实例，涵盖了医学图像处理、文字识别和自导引小车路径识别等应用实例，并结合理论算法，提供了大量MATLAB代码程序，以帮助读者掌握如何使用MATLAB语言快速进行算法的仿真、调试和估计等方法。第11章～第12章，是两个综合性较强的实例，分别是Visual C++实现的基于神经网络的文字识别系统和车牌定位系统。 本书附带的光盘给出了各个章节列举的实例的源代码，同时赠送了28个常用数字图像处理算法的Visual C++代码实现。 本书讲解深入浅出，实例程序丰富，注重理论与实践相结合。本书可作为计算机应用、自动化、图像处理与模式识别、机电一体化专业的高年级本科生或研究生的参考书，也可供从事图像处理与识别的研究人员和工程技术人员阅读参考。(This book introduces the basic principles of image processing and recognition of the typical methods and practical skills . The book is divided into 12 chapters , Chapter 1- Chapter 6 is the basis of the content of image processing and recognition , including images scientific overview , MATLAB programming language images, image enhancement, image segmentation, image feature extraction and image recognition Chapter 7- Section Chapter 10 is a project example image processing and recognition , covering the medical image processing, character recognition and self- guided trolley path recognition example , the combination of theory and algorithms, for a lot of MATLAB code procedures to help readers learn how to use MATLAB language fast simulation , debugging and estimation methods algorithm. Chapter 11- Chapter 12 , are two examples of highly integrated , namely Visual C++ implementation based on neural network character recognition system and a license plate positioning system. The book)

C++ based JPEG on grayscale image DCT -> Quantization -> ZigZag -> DPCM, Runlength,-> Entropy Coding

平常经常用到的一个小程序，两个矩阵相乘的求法(Common often USES a small program, two of the matrix multiplication is also given)

说明：  SIFT算法的区域匹配，比原有的SIFT+KD树匹配更加高效、准确。(different area SIFT algorithm which is better than the original SIFT and KD tree match.)

基于opencv的C语言的疲劳驾驶检测代码，肤色检测人脸(fatigue driving detecting)