颜色直方图匹配算法实现 环境：opencv2.31，vs2008平台 绝对可用

用MATLAB提取灰度直方图的特征，如能量、均值、方差等。

把原始图像的灰度直方图从比较集中的某个灰度区间变成在全部灰度范围内的均匀分布。对图像进行非线性拉伸，重新分配图像像素值，使一定灰度范围内的像素数量大致相同。把给定图像的直方图分布改变成“均匀”分布直方图分布。

基于直方图的阈值分割的matlab实现十分简洁，效果也不错。

Algorithm Description Recognizing objects from large image databases, histogram based methods have proved simplicity and usefulness in last decade. Initially, this idea was based on color histograms that were launched by swain [1]. This algorithm presents the first part of our proposed technique named as “Histogram processed Face Recognition” [2] For training, grayscale images with 256 gray levels are used. Firstly, frequency of every gray-level is computed and stored in vectors for further processing. Secondly, mean of consecutive nine frequencies from the stored vectors is calculated and are stored in another vectors for later use in testing phase. This mean vector is used for calculating the absolute differences among the mean of trained images and the test image. Finally the minimum difference found identifies the matched class with test image. Recognition accuracy is of 99.75% (only one mis-match i.e. recognition fails on image number 4 of subject 17) [1] M. J. Swain and D. H. Ballard, “Indexing via color histogram”, In Proceedings of third international conference on Computer Vision (ICCV), pages 390–393, Osaka, Japan, 1990. [2] Fazl-e-Basit, Younus Javed and Usman Qayyum, "Face Recognition using processed histogram and phase only correlation ", 3rd IEEE International Conference on Emerging Technology pp. 238-242

该代码对图像灰度直方图的计算均采用了概率统计的方式，只是在统计的过程中采用了三种不同的统计算法，最后还比较了这三种不同的统计算法各自在计算过程中所花费的时间。建议用户与“图像灰度直方图计算的Matlab代码”进行比较学习。

直方图均衡化C++代码

点运算及直方图几何运算频率变换图像腐蚀膨胀开闭运算

基于matlab的直方图均衡化的彩色图像增强程序，通过matlab仿真，比较均衡化后的还原图像与输入原始真彩色图像，输出图像轮廓更清晰，亮度明显增强

利用C++ 实现真彩图的直方图均衡化，灰度图同样也适用。程序分别对B G R 三个通道分别均衡化，并画出均衡化每个通道的前后对比图