sift_point

基于小波分解重构的图像去噪程序 请多指教(Based on wavelet decomposition and reconstruction of image denoising program please enlighten)

关于分水岭图像分割的源码，需要的可以下来用(watershed image segmentation on the source need to be down with the)

小波包学习资料,包括多种小波包应用的MALAB算法实现( good learning materials about WPD ,including many MALAB algorithms of wavelet packet )

说明：  区域生长法的图像分割，可以较好地提取脑部图像。(The region growing method can extract brain image well.)

使用经典边缘检测算子进行边缘检测，加入噪声的图像边缘检测结果和未加噪声的图像检测结果进行对比分析，默认阈值设置的图像检测结果和手动设置阈值的图像检测结果进行对比(Edge detection using classical edge detection operator)

在统计学中，皮尔逊积矩相关系数（英语：Pearson product-moment correlation coefficient，又称作 PPMCC或PCCs[1], 文章中常用r或Pearson s r表示）用于度量两个变量X和Y之间的相关（线性相关），其值介于-1与1之间。在自然科学领域中，该系数广泛用于度量两个变量之间的相关程度。它是由卡尔·皮尔逊从弗朗西斯·高尔顿在19世纪80年代提出的一个相似却又稍有不同的想法演变而来的。这个相关系数也称作“皮尔森相关系数r”。(In statistics, Pearson product-moment correlation coefficient (English: Pearson product-moment correlation coefficient, also known PPMCC or PCCs [1], the article used or Pearson s r r representation) is a measure of the two variables X and Y between correlation (linear correlation), a value between-1 and 1. In the natural sciences, the coefficient is widely used to measure the degree of correlation between the two variables. It is made a similar Karl Pearson Francis Galton in the 1880s but made a slightly different idea evolved. The correlation coefficient is also called Pearson s correlation coefficient r .)

说明：  多尺度分水岭变换分割图像，利用此编码可以编程计算在不同程度有效分割图像。(Image Segmentation)

matlab实现的图像金字塔，过程很清晰，简单易学(matlab implementation of image pyramid, the process is very clear and easy to learn)

sift算法图像特征提取，用于图像定位及目标跟踪(Using SIFT algorithm to extract image feature)

DELPHI编写灰度直方图显示，DELPHI编写灰度直方图显示(DELPHI Writing gray histogram display, DELPHI written grayscale histogram display)