ColorImageProcessing

超分辨率重构，介绍超分辨率重构最大后验概率算法。(Super-resolution reconstruction, introduced the super-resolution reconstruction of maximum a posteriori probability algorithm.)

图像亮度和对比度调节，已验证，分别有亮度调节和对比度调节的调整框(Image brightness and contrast adjustment, has been verified, respectively, brightness adjustment and contrast ad)

sift角点检测及匹配，在不同尺度空间下的角点检测方法，很方便实用。(sift corner detection and matching, the corner detection methods under different scales of space, it is convenient and practical.)

说明：  jm软件源码，用于流媒体编码标准H264的性能评测(jm-source software for streaming media encoding standard of the performance evaluation H264)

在vs平台下利用opencv程序，可以处理图像的膨胀、腐蚀以及开闭运算。(Availability v Understanding use of open platform opencv degree, permissible processing, swelling, corrosion and opening and closing operation)

在VC6.0环境下，借助openCV函数库，对视频中的人脸进行检测，并实时跟踪，然后用Haar特征的分类器定位人脸，并检测瞳孔存在与否，提取关于眼睛的疲劳信息(In VC6.0 environment, with the aid of openCV function library, to detect human faces in video, and real-time tracking, positioning and use characteristics of Haar classifier faces, and examine the pupil existence, to extract the information about the fatigue of the eye)

第8章 图象的检测及模板匹配 图象的分割与检测(识别)实际上是一项非常困难的工作。很难说清楚为什么图象应该分割成这样而不是那样。(Chapter 8 Detection of image template matching and image segmentation and detection (recognition) is actually a very difficult task. It is hard to say clearly why the image should be divided into such and not as.)

人民邮电出版社，《VC++数字图像处理典型算法及实现》-- 第9章图像分割。 包括图像的并行边界分割、串行边界分割、并行区域分割、串行区域分割、Canny算子的实现等源代码(Posts )

本程序可以使用图像处理算法对图片进行处理，对于一个含有各种物体的图片，如米粒，可以计算物体的个数。使用前请配置好opencv环境。希望对大家学习图像处理和opencv有所帮助...(This procedure can use the image processing algorithm for processing images for a picture with a variety of objects, such as a grain of rice, can calculate the number of objects. Configured before use opencv environment. We would like to learn opencv image processing and help ...)

EDLines是一种快速直线检测算子，2012年在ICCV上提出，也是目前处理直线检测最快的算法之一，该算法包含三个步骤：（1）边缘提取：利用Edge Drawing (ED)算法[28,29]从灰度图像中提取边缘片段；（2）线段检测：利用最小二乘法提取直线段；（3）线段确认：遵循Helmholtz定律，从已提取的直线段中摒弃虚假线段。EDLines算法的优越性得益于Edge Drawing (ED)算法能够从灰度图像中准确、快速、稳定地提取出光滑、完整的边缘片段。Edge Drawing (ED)算法包含以下步骤： （1）采用size=5*5,σ=1的高斯核对灰度图像进行平滑滤波，去除噪声； （2）采用一种常用的梯度算子，如Prewitt、Sobel或Scharr等计算平滑后图像中每个像素点的梯度幅度和梯度方向； （3）将梯度图中邻域内幅度最大的像素点标记为锚点，这些锚点成为图像边缘点(edge elements)的概率很大； （4）将相邻的锚点连接成边缘线。从一个锚点起始，ED利用相邻像素的梯度幅度和方向在梯度为最大值的锚点之间游走。 (Edge Drawing (ED) is our recently-proposed, novel, fast edge detection algorithm. What makes ED stand out the existing edge detectors, e.g., Canny, is the following: While the other edge detectors give out a binary edge image as output, where the etected edge pixels are usually independent, discontinuous entities ED produces a set of edge segments, which are clean, contiguous, i.e., connected, chains of edge pixels. Thus, while the output of the other edge detectors requires urther processing to generate potential object boundaries, which may not even be possible or result in inaccuracies ED not only produces perfectly connected object boundaries by default, but it also achieves this in blazing speed compared to other edge detector.)