DRP4_13p

数字图像处理中利用matlab完成图像复原、彩色变换、边缘检测、二值图像细化和伪彩色处理(Digital image processing complete use of matlab image restoration, color transformation, edge detection, binary image refining and false color processing)

请拾取第一根线(LINE)或弧(ARC)连接线段或圆弧 ,再拾取第二根线(LINE)或弧(ARC)进行连接(Please pick up the first lines (LINE) or arc (ARC) connection line or arc, then picked up second line (LINE) or arc (ARC) connection)

it is useful to my research work

基于PWM变换技术的电力电子变压器，主要介绍了电力电子变压器及其在MATLAB环境下的仿真。(Research PWM conversion technology based on power electronic transformer of MATLAB.)

克里金法假定采样点之间的距离或方向可以反映可用于说明表面变化的空间相关性。克里金法工 具可将数学函数与指定数量的点或指定半径内的所有点进行拟合以确定每个位置的输出值。克里金法是一个多步过程；它包括数据的探索性统计分析、变异函数建模 和创建表面，还包括研究方差表面。当您了解数据中存在空间相关距离或方向偏差后，便会认为克里金法是最适合的方法。该方法通常用在土壤科学和地质中。(This function completes what I have started with the functions variogram and variogramfit. It is not intended to be a highly optimized code for performing kriging but may have educational value. Note that for using kriging, you should download the latest version of variogramfit. kriging uses ordinary kriging to interpolate a variable z measured at locations with the coordinates x and y at unsampled locations xi, yi. The function requires the variable vstruct that contains all necessary information on the variogram. vstruct is the forth output argument of the function variogramfit. This is a rudimentary, but easy to use function to perform an ordinary kriging interpolation. I call it rudimentary since it always includes ALL observations to estimate values at unsampled locations. This may not be necessary when sample locations are not within the autocorrelation range but would require something like a k nearest neighbor search algorithm or something similar. Thus, the alg)

some of the important matlab codes for calculating PSD of a signal for students

该程序可以实现图片的拖拽，放大和缩小等简单的处理。(The program can drag images, zoom in and out such simple treatment.)

matlab里面的一些简化工具包，便携易带，ncm工具包，还有一些教学演示程序(the matlab toolbox simple!please,if you want to show something,you can use it)

说明：  用c语言实现matlab，毕业设计很有用(C language with matlab, graduate design useful)

Most recent approaches have posed texture synthesis in a statistical setting as a problem of sampling from a probability distribution. Zhu et. al. [12] model texture as a Markov Random Field and use Gibbs sampling for synthesis. Unfortunately, Gibbs sampling is notoriously slow and in fact it is not possible to assess when it has converged. Heeger and Bergen [6] try to coerce a random noise image.