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

本程序展开了雾霾天气下图像清晰化的技术讨论，雾天图像的清晰化技术，采用matlab编程，有详细的注释和图片资源，能够直接运行！

计算矩阵各列与其他各列的互相关系数，并按照概率分布利用直方图的形式展示出来。 该互相关系数的定义参考Michael Elad的《Sparse and Redundant Representations》p25 Definition2.3

基于直方图的灰度图二值化程序，可以针对图片，自动寻找一个合适的阈值进行二值化，自己编写的，基于Matlab的程序。

里面包含大量直方图均衡化算法，部分程序有对应的论文。只有glg算法是opencv的，其他都是matlab程序，内含CLAHE,AHE,MMBEBHE,RMSHE,RSIHE,BBHE,DSIHE,权重的直方图均衡化，等等。