爱乐眼底图像分析

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2012-6-3 17:13| 发布者: gbs| 查看: 2647| 评论: 0

摘要: 一:数学背景首先看一下一维的微分公式Δf = f(x+1) – f(x), 对于一幅二维的数字图像f(x,y)而言,需要完成XY两个方向上的微分,所以有如下的公式:分别对X,Y两个方向上求出它们的偏微分,最终得到梯度Delta F.对于 ...
一:数学背景

首先看一下一维的微分公式Δf = f(x+1) – f(x), 对于一幅二维的数字图像f(x,y)而言,需要完

成XY两个方向上的微分,所以有如下的公式:



分别对X,Y两个方向上求出它们的偏微分,最终得到梯度Delta F.

对于离散的图像来说,一阶微分的数学表达相当于两个相邻像素的差值,根据选择的梯度算

子不同,效果可能有所不同,但是基本原理不会变化。最常见的算子为Roberts算子,其它

常见还有Sobel,Prewitt等算子。以Roberts算子为例的X,Y的梯度计算演示如下图:




二:图像微分应用

图像微分(梯度计算)是图像边缘提取的重要的中间步骤,根据X,Y方向的梯度向量值,可以

得到如下两个重要参数振幅magnitude, 角度theta,计算公式如下:



Theta = tan-1(yGradient/xGradient)

magnitude表示边缘强度信息

theta预言边缘的方向走势。

假如对一幅数字图像,求出magnitude之后与原来每个像素点对应值相加,则图像边缘将被

大大加强,轮廓更加明显,是一个很典型的sharp filter的效果。



三:程序效果

X, Y梯度效果,及magnitude效果



图像微分的Sharp效果:



四:程序源代码

package com.process.blur.study;

import java.awt.image.BufferedImage;

// roberts operator
// X direction 1, 0
// 0,-1
// Y direction 0, 1
// -1, 0

public class ImageGradientFilter extends AbstractBufferedImageOp {
public final static int X_DIRECTION = 0;
public final static int Y_DIRECTION = 2;
public final static int XY_DIRECTION = 4;

private boolean sharp;
private int direction;

public ImageGradientFilter() {
direction = XY_DIRECTION; // default;
sharp = false;
}

public boolean isSharp() {
return sharp;
}

public void setSharp(boolean sharp) {
this.sharp = sharp;
}

public int getDirection() {
return direction;
}

public void setDirection(int direction) {
this.direction = direction;
}

@Override
public BufferedImage filter(BufferedImage src, BufferedImage dest) {
int width = src.getWidth();
int height = src.getHeight();

if (dest == null )
dest = createCompatibleDestImage( src, null );

int[] inPixels = new int[width*height];
int[] outPixels = new int[width*height];
getRGB( src, 0, 0, width, height, inPixels );
int index = 0;
double mred, mgreen, mblue;
int newX, newY;
int index1, index2, index3;
for(int row=0; row
int ta = 0, tr = 0, tg = 0, tb = 0;
for(int col=0; col
index = row * width + col;

// base on roberts operator
newX = col + 1;
newY = row + 1;
if(newX > 0 && newX < width) {
newX = col + 1;
} else {
newX = 0;
}

if(newY > 0 && newY < height) {
newY = row + 1;
} else {
newY = 0;
}
index1 = newY * width + newX;
index2 = row * width + newX;
index3 = newY * width + col;
ta = (inPixels[index] >> 24) & 0xff;
tr = (inPixels[index] >> 16) & 0xff;
tg = (inPixels[index] >> 8) & 0xff;
tb = inPixels[index] & 0xff;

int ta1 = (inPixels[index1] >> 24) & 0xff;
int tr1 = (inPixels[index1] >> 16) & 0xff;
int tg1 = (inPixels[index1] >> 8) & 0xff;
int tb1 = inPixels[index1] & 0xff;

int xgred = tr -tr1;
int xggreen = tg - tg1;
int xgblue = tb - tb1;

int ta2 = (inPixels[index2] >> 24) & 0xff;
int tr2 = (inPixels[index2] >> 16) & 0xff;
int tg2 = (inPixels[index2] >> 8) & 0xff;
int tb2 = inPixels[index2] & 0xff;

int ta3 = (inPixels[index3] >> 24) & 0xff;
int tr3 = (inPixels[index3] >> 16) & 0xff;
int tg3 = (inPixels[index3] >> 8) & 0xff;
int tb3 = inPixels[index3] & 0xff;

int ygred = tr2 - tr3;
int yggreen = tg2 - tg3;
int ygblue = tb2 - tb3;

mred = Math.sqrt(xgred * xgred + ygred * ygred);
mgreen = Math.sqrt(xggreen * xggreen + yggreen * yggreen);
mblue = Math.sqrt(xgblue * xgblue + ygblue * ygblue);
if(sharp) {
tr = (int)(tr + mred);
tg = (int)(tg + mgreen);
tb = (int)(tb + mblue);
outPixels[index] = (ta << 24) | (clamp(tr) << 16) | (clamp(tg) << 8) | clamp(tb);
} else {
outPixels[index] = (ta << 24) | (clamp((int)mred) << 16) | (clamp((int)mgreen) << 8) | clamp((int)mblue);
// outPixels[index] = (ta << 24) | (clamp((int)ygred) << 16) | (clamp((int)yggreen) << 8) | clamp((int)ygblue);
// outPixels[index] = (ta << 24) | (clamp((int)xgred) << 16) | (clamp((int)xggreen) << 8) | clamp((int)xgblue);
}


}
}
setRGB(dest, 0, 0, width, height, outPixels );
return dest;
}

public static int clamp(int c) {
if (c < 0)
return 0;
if (c > 255)
return 255;
return c;
}
}


http://blog.csdn.net/jia20003/article/details/7562092



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