dct

虹膜识别的内外边缘图像分割，包括瞳孔的分离，还有外边界的分离(Iris recognition both inside and outside the edge of image segmentation, including the separation of the pupil, as well as outside the borders separating)

程序可以提取点云数据的特征点，提取三维散乱点云的边界(Program point cloud data can be extracted feature points extracted boundary of D Scattered Clouds)

噪声估计法，先定位噪声点，再实行去噪处理(Noise estimation method, first locate the noise points, then implement denoising.)

rgb to HSV，很有用，调试成功，可以应用(rgb to HSV，#include "stdafx.h" #include <iostream.h> void main(float R, float G, float B, float& H, float& S, float&V) { // r,g,b values are from 0 to 1 // h = [0,360], s = [0,1], v = [0,1] // if s == 0, then h =-1 (undefined) float min, max, delta,tmp tmp = R>G?G:R min = tmp>B?B:tmp tmp = R>G?R:G max = tmp>B?tmp:B V = max // v delta = max- min if( max != 0 ) S = delta/max // s else { // r = g = b = 0 // s = 0, v is undefined S = 0 H = 0 return } if (delta == 0){ H = 0 return } else if(R == max){ if (G >= B) H = (G- B)/delta // between yellow & magenta else H = (G- B)/delta+ 6 } else if( G == max ) H = 2+ ( B- R )/delta // between cyan & yellow else if (B == ma)

卡尔曼滤波是一种高效率的递归滤波器(自回归滤波器), 它能够从一系列的不完全及包含噪声的测量中，估计动态系统的状态。(Calman filtering is an efficient recursive filter (autoregressive filter). It can estimate the state of dynamic system from a series of incomplete and noisy measurements.)

说明：  基于SURF算法实现图像特征提取与描述，使用RANSAC进行图像细配准(SURF algorithm was used to extract and describe image features, and RANSAC was used for image registration)

几种经典的图像平滑去噪算法程序，其中包括小波软阈值和硬阈值处理。还附送三个图像质量评价程序(several classic image denoising smoothing algorithm procedures, which include thresholding wavelet soft and hard threshold processing. Also attached three image quality evaluation procedures)

RunSFM中作者写的纹理映射的应用程序(RunSFM the authors write texture mapping application)

说明：  基于opencv的小面积区域的去除.可以去除面积小于指定面积的无兴趣区域.(Opencv-based small area of the region to remove. Can remove an area of less than the specified area is not interested in the region.)

说明：  按照最邻近原则把待分类样本点分到各个簇。然后按平均法重新计算各个簇的质心，从而确定新的簇心。一直迭代，直到簇心的移动距离小于某个给定的值。(According to the nearest neighbor principle, the sample points to be classified are divided into clusters. Then the centroid of each cluster is recalculated according to the average method to determine the new cluster center. Iterate until the moving distance of the cluster center is less than a given value)