dianziwenxiang

一幅大小为256×256的256级灰度图像（每像素8bit） 如果图像的各个像素之间是统计独立的，求该图像的信息熵 -离散无记忆信源 如果图像的各个像素之间不是统计独立的，求该图像的信息熵 -图像像素的概率分布模型(A 256 gray scale image (8bit per pixel) with a size of 256 x 256 If each pixel of the image is statistically independent, the information entropy of the image is calculated Discrete memoryless source If each pixel of the image is not statistically independent, the information entropy of the image is calculated Probability distribution model of image pixels)

实现凸多边形排样，使用临界多边形NFP判断两个凸多边形形是否相交,使用启发式算法提高效率， (Convex polygon nesting, polygon NFP to determine whether two convex polygon-shaped intersection, the use of heuristic algorithms to improve efficiency,)

该源码为目标检测和跟踪算法 包括： 1. 静态背景下的背景预测法目标检测 2. 静态背景下帧间差分法目标检测 3. Mean Shift目标跟踪方法 4. 重心多目标跟踪方法 (The source code for target detection and tracking algorithm include: 1 Static background background prediction target detection 2 Static background frame difference method for target detection 3. Mean Shift tracking method 4 multi-target tracking methods focus)

摄影测量中影像匹配小程序，附带两幅图片,比较实用(Photogrammetry image matching small program, with two pictures, more practical)

这块程序是用来求取圆面和矩形面积的，分为几个子程序。(This procedure is used to strike a round face and a rectangular area, divided into several subroutines.)

通过对二维人脸图片的特征提取进行三维人脸的重建(Through the two-dimensional face image feature extraction for three-dimensional reconstruction of human faces)

天津工业大学课程设计，内容包含vs2010对图像的灰度化，二值化，边缘检测。包含单纯c语言编写的对图像的灰度化，二值化，边缘检测的一个.C文件，可以直接用vc6.0运行，也可以直接在DSP上运行，还包含一份详细的设计报告。(Tianjin Polytechnic University curriculum design, content includes vs2010 grayscale image binarization, edge detection. Contains pure c language for image graying, binarization, edge detection is one. C file that can be run with vc6.0 can also be run directly on a DSP, also contains a detailed design report.)

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)

运用基于密度的聚类算法实现了超像素的分割(The segmentation of super pixels is realized by using the clustering algorithm based on density)

以SIFT特征为目标输入，LDAHash方法进行降维实现目标识别(SIFT features input LDAHash method to reduce the dimensionality of the target recognition)