有关EMD分解matlab程序

一般的的查询可在matlab里的帮助界面进行搜索，点击帮助。

打开帮助页面，左侧检索栏进行检索需要查询的语句，然后即可查看右侧查询结果。

或者在主界面，输入help 空格+你要查询的内容，进行查询。下次你可以尝试一下。

一般程序都会有不懂得语句，或没用过的，可以在刚刚说过的帮助页面进行查询，看如何使用，输入参量什么意义，程序输出结果是什么。

你这个描述太过于简略了，我只能给出这样的答案了，一般可以都给一点程序，也许会有对答题人帮助。

不过你这个程序的确有点复杂，不根据前后逻辑，和主程序和目的是很难解答的。

你可以看看主程序，再查查帮助。

希望对你有所帮助。谢谢。

整个项目的结构图：

编写DetectFaceDemo.java,代码如下：

[java] view

plaincopyprint?

package com.njupt.zhb.test

import org.opencv.core.Core

import org.opencv.core.Mat

import org.opencv.core.MatOfRect

import org.opencv.core.Point

import org.opencv.core.Rect

import org.opencv.core.Scalar

import org.opencv.highgui.Highgui

import org.opencv.objdetect.CascadeClassifier

//

// Detects faces in an image, draws boxes around them, and writes the results

// to "faceDetection.png".

//

public class DetectFaceDemo {

public void run() {

System.out.println("\nRunning DetectFaceDemo")

System.out.println(getClass().getResource("lbpcascade_frontalface.xml").getPath())

// Create a face detector from the cascade file in the resources

// directory.

//CascadeClassifier faceDetector = new CascadeClassifier(getClass().getResource("lbpcascade_frontalface.xml").getPath())

//Mat image = Highgui.imread(getClass().getResource("lena.png").getPath())

//注意：源程序的路径会多打印一个‘/’，因此总是出现如下错误

/*

* Detected 0 faces Writing faceDetection.png libpng warning: Image

* width is zero in IHDR libpng warning: Image height is zero in IHDR

* libpng error: Invalid IHDR data

*/

//因此，我们将第一个字符去掉

String xmlfilePath=getClass().getResource("lbpcascade_frontalface.xml").getPath().substring(1)

CascadeClassifier faceDetector = new CascadeClassifier(xmlfilePath)

Mat image = Highgui.imread(getClass().getResource("we.jpg").getPath().substring(1))

// Detect faces in the image.

// MatOfRect is a special container class for Rect.

MatOfRect faceDetections = new MatOfRect()

faceDetector.detectMultiScale(image, faceDetections)

System.out.println(String.format("Detected %s faces", faceDetections.toArray().length))

// Draw a bounding box around each face.

for (Rect rect : faceDetections.toArray()) {

Core.rectangle(image, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height), new Scalar(0, 255, 0))

}

// Save the visualized detection.

String filename = "faceDetection.png"

System.out.println(String.format("Writing %s", filename))

Highgui.imwrite(filename, image)

}

}

package com.njupt.zhb.test

import org.opencv.core.Core

import org.opencv.core.Mat

import org.opencv.core.MatOfRect

import org.opencv.core.Point

import org.opencv.core.Rect

import org.opencv.core.Scalar

import org.opencv.highgui.Highgui

import org.opencv.objdetect.CascadeClassifier

//

// Detects faces in an image, draws boxes around them, and writes the results

// to "faceDetection.png".

//

public class DetectFaceDemo {

public void run() {

System.out.println("\nRunning DetectFaceDemo")

System.out.println(getClass().getResource("lbpcascade_frontalface.xml").getPath())

// Create a face detector from the cascade file in the resources

// directory.

//CascadeClassifier faceDetector = new CascadeClassifier(getClass().getResource("lbpcascade_frontalface.xml").getPath())

//Mat image = Highgui.imread(getClass().getResource("lena.png").getPath())

//注意：源程序的路径会多打印一个‘/’，因此总是出现如下错误

/*

* Detected 0 faces Writing faceDetection.png libpng warning: Image

* width is zero in IHDR libpng warning: Image height is zero in IHDR

* libpng error: Invalid IHDR data

*/

//因此，我们将第一个字符去掉

String xmlfilePath=getClass().getResource("lbpcascade_frontalface.xml").getPath().substring(1)

CascadeClassifier faceDetector = new CascadeClassifier(xmlfilePath)

Mat image = Highgui.imread(getClass().getResource("we.jpg").getPath().substring(1))

// Detect faces in the image.

// MatOfRect is a special container class for Rect.

MatOfRect faceDetections = new MatOfRect()

faceDetector.detectMultiScale(image, faceDetections)

System.out.println(String.format("Detected %s faces", faceDetections.toArray().length))

// Draw a bounding box around each face.

for (Rect rect : faceDetections.toArray()) {

Core.rectangle(image, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height), new Scalar(0, 255, 0))

}

// Save the visualized detection.

String filename = "faceDetection.png"

System.out.println(String.format("Writing %s", filename))

Highgui.imwrite(filename, image)

}

}

3.编写测试类：

[java] view

plaincopyprint?

package com.njupt.zhb.test

public class TestMain {

public static void main(String[] args) {

System.out.println("Hello, OpenCV")

// Load the native library.

System.loadLibrary("opencv_java246")

new DetectFaceDemo().run()

}

}

//运行结果:

//Hello, OpenCV

//

//Running DetectFaceDemo

///E:/eclipse_Jee/workspace/JavaOpenCV246/bin/com/njupt/zhb/test/lbpcascade_frontalface.xml

//Detected 8 faces

//Writing faceDetection.png

package com.njupt.zhb.test

public class TestMain {

public static void main(String[] args) {

System.out.println("Hello, OpenCV")

// Load the native library.

System.loadLibrary("opencv_java246")

new DetectFaceDemo().run()

}

}

//运行结果:

//Hello, OpenCV

//

//Running DetectFaceDemo

///E:/eclipse_Jee/workspace/JavaOpenCV246/bin/com/njupt/zhb/test/lbpcascade_frontalface.xml

//Detected 8 faces

//Writing faceDetection.png

function imf = emd(x,n)%%最好把函数名改为emd1之类的，以免和Grilling的emd冲突

%%n为你想得到的IMF的个数

c = x('% copy of the input signal (as a row vector)

N = length(x)-

% loop to decompose the input signal into n successive IMFs

imf = []% Matrix which will contain the successive IMF, and the residuefor t=1:n

% loop on successive IMFs

%-------------------------------------------------------------------------

% inner loop to find each imf

h = c% at the beginning of the sifting process, h is the signal

SD = 1% Standard deviation which will be used to stop the sifting process

while SD >0.3 % while the standard deviation is higher than 0.3 (typical value) %%筛选停止准则

% find local max/min points

d = diff(h)% approximate derivative %%求各点导数

maxmin = []% to store the optima (min and max without distinction so far)

for i=1:N-2

if d(i)==0% we are on a zero %%导数为0的点，即”驻点“，但驻点不一定都是极值点，如y=x^3的x=0处

if sign(d(i-1))~=sign(d(i+1)) % it is a maximum %%如果驻点两侧的导数异号（如一边正，一边负），那么该点为极值点

maxmin = [maxmin, i]%%找到极值点在信号中的坐标（不分极大值和极小值点）

end

elseif sign(d(i))~=sign(d(i+1)) % we are straddling a zero so%%如y=|x|在x=0处是极值点，但该点倒数不存在，所以不能用上面的判

断方法

maxmin = [maxmin, i+1] % define zero as at i+1 (not i) %%这里提供了另一类极值点的判断方法

end

end

if size(maxmin,2) <2 % then it is the residue %%判断信号是不是已经符合残余分量定义

break

end

% divide maxmin into maxes and mins %% 分离极大值点和极小值点

if maxmin(1)>maxmin(2) % first one is a max not a min

maxes = maxmin(1:2:length(maxmin))

mins = maxmin(2:2:length(maxmin))

else% is the other way around

maxes = maxmin(2:2:length(maxmin))

mins = maxmin(1:2:length(maxmin))

end% make endpoints both maxes and mins

maxes = [1 maxes N]

mins = [1 mins N]

%------------------------------------------------------------------------- % spline interpolate to get max and min envelopesform imf

maxenv = spline(maxes,h(maxes),1:N) %%用样条函数插值拟合所有的极大值点

minenv = spline(mins, h(mins),1:N)%%用样条函数插值拟合所有的极小值点

m = (maxenv + minenv)/2% mean of max and min enveloppes %%求上下包络的均值

prevh = h% copy of the previous value of h before modifying it %%h为分解前的信号

h = h - m% substract mean to h %% 减去包络均值

% calculate standard deviation

eps = 0.0000001% to avoid zero values

SD = sum ( ((prevh - h).^2) ./ (prevh.^2 + eps) )%% 计算停止准则

end

imf = [imfh]% store the extracted IMF in the matrix imf

% if size(maxmin,2)<2, then h is the residue

% stop criterion of the algo. if we reach the end before n

if size(maxmin,2) <2

break

end

c = c - h% substract the extracted IMF from the signal

end

return

参考资料

http://zhidao.baidu.com/link?url=Dv2ef87TOGx8zcbCT1UJsZ2kutWrm4FuT5kbMZY5mAAn5yv7APibQ1y8fSag5JvbF2fKlI5jhgpTXu95SDRgi_

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