fenjiechonggou

说明：  通过IDL，简单实现灰度变换，彩色变换等基本操作，包括图片打开保存拖拽显示(Simple implementation of gray level transformation, color transformation and other basic operations)

用于Landsat5 TM数据的辐射定标(Calibration for Landsat TM data using IDL)

Bandelet变换的图像处理工具箱，包括图像处理的源程序(Bandelet transform image processing toolbox, including image processing of the source)

数字图像处理中对分类器优劣的判定常用的ROC/ACU源码。希望对你有用。(The ROC/ACU source code commonly used in the determination of the quality of the classifier in digital image processing. I hope useful to you.)

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 .)

图像混色源码例程,程序结合易语言应用接口支持库和位图操作支持库，调用API函数实现图像的混色效果。(Source image blending routines, combined with easy language application program interface to support libraries and bitmap operations support library , call the API functions for image blending effect .)

衡量图像去噪和SAR图像降斑效果的一些指标，psnr，snr，enl(enl（Equivalent number of looks）)

根据指定字符分割字符串，VC++精选编程源码，很好的参考。(According to the specified character segmentation character string, VC++ select programming source code, a good reference.)

Mallat算法和Atrous算法实现任意一维信号和二维图像的分解与重构。(Mallat algorithm and Atrous algorithm to achieve the arbitrary one-dimensional signals and two-dimensional image decomposition and reconstruction.)

Image segmentation systems have the potential to dramatically improve the performance of Digital image processing. Segmentation procedures partition an image into its constituent parts or objects. In general, segmentation is one of the most difficult tasks in digital image processing. Detection of salient image regions is useful for applications like image segmentation, adaptive compression, and region-based image retrieval. This problem can be tackled by mapping the pixels into various feature spaces, which are subjected to various grouping algorithms It present a novel method to determine salient regions in images using low-level features of luminance and color. The method is fast, easy to implement and generates high quality saliency maps of the same size and resolution as the input image.