diskreet.m

蔡远利，西安交大，随机滤波与最优估计，非线性滤波，卡尔曼滤波，最小方差估计(optimal estimation)

将图片由RGB格式转化为YCbCr格式下，用椭圆方法判定肤色和非肤色区域，进行二值化处理，最后显示结果(Pictures from RGB format into a YCbCr format, the method used to determine color elliptical and non-color regions, to carry out binarization processing, the final result will be displayed)

说明：  合成孔径雷达CS成像算法，运用该算法获得成像结果(CS synthetic aperture radar imaging algorithm, imaging results obtained using this algorithm)

采用改进的CV图像分割算法用于提取图像边缘轮廓，代码注释很详细。(CV improved image segmentation algorithm for extracting image edge profile, the code is very detailed notes.)

matlab 高动态图像 读取的hdr格式的读取文件，经测试可以用(matlab read hdr high dynamic image to read the file format, the test can be used)

最小二乘图像影像匹配方法，一个很详细的源码例程，有过程有结果，希望对初学者有帮助。高斯解方程，以及最小二乘匹配，里面都很详细。(Squares image image matching method, a very detailed source code routines, the outcome of the process, I hope to help beginners. Gaussian solution of equations, and least squares matching, which is very detailed.)

实现基于颜色的Kmeans图像分割，只是需要自己设定聚类中心数目，聚类中心初始化采用Kmeans++(Kmeans image segmentation based on color, but need to set the number of cluster centers, clustering center initialization using Kmeans++)

说明：  一、主要研究内容 1.基本内容 本设计目标在于利用Matlab强大的图像处理能力和实用便捷的编程方法，通过处理包含人脸的视频帧系列图像，灰度积分投影技术的眼睛定位方法，进而利用perclos计数，计算眨眼率，从而得到比较准确的疲劳状况。包括lun文初稿撰写。 2.具体要求 本设计基于灰度积分投影技术的眼睛定位方法，再结合perclos技术。首先通过图像预处理技术得到灰度分配较为均匀的图像,然后分别利用水平和垂直灰度积分投影曲线结合人脸的结构特征找到眼睛的位置坐标,实现了准确的眼睛定位,通过perclos技术技术眨眼率，根据先验值得到是否疲劳。 二、算法原理 视频输入——预处理去除非人脸区域——人脸定位——人眼定位（灰度积分投影）——眼睛张合度——perclos技术统计——输出结果 三、GUI界面设计 [需要有一定编程功底](1. Basic contents The goal of this design is to use the powerful image processing ability of MATLAB and the practical and convenient programming method, through processing the video frame series images including face, the eye location method of gray integral projection technology, and then using PERCLOS count to calculate the blink rate, so as to get a more accurate fatigue condition. Including the first draft of Lun.)

基于matlab实现干涉显微镜图像重构中的二维解包裹函数。( QualityGuidedUnwrap2D implements 2D quality guided path following phase unwrapping algorithm. Inputs: 1. Complex image in .mat double format 2. Binary mask (optional) Outputs: 1. Unwrapped phase image 2. Phase quality map This code can easily be extended for 3D phase unwrapping. Technique adapted from: D. C. Ghiglia and M. D. Pritt, Two-Dimensional Phase Unwrapping: Theory, Algorithms and Software. New York: Wiley-Interscience, 1998. Posted by Bruce Spottiswoode on 22 December 2008 2010/07/23 Modified by Carey Smith)

计算图像的多重分形谱。它是分形的多尺度衡量的分型参数。(Calculated images multifractal spectrum. It is a multi-fractal measure the sub-type parameters.)