图像处理之基于Otsu阈值实现图像二值化
一：基本原理
该方法是图像二值化处理常见方法之一，在Matlab与OpenCV中均有实现。
Otsu Threshing方法是一种基于寻找合适阈值实现二值化的方法，其最重
要的部分是寻找图像二值化阈值，然后根据阈值将图像分为前景（白色）
或者背景（黑色）。假设有6×6的灰度图像，其像素数据及其对应的直方
图如下图：
阈值寻找方法首先假设是为T=3，则背景像素的比重、均值、方差的计算
结果如下：
根据前景像素直方图，计算比重、均值、方差的过程如下：
上述整个计算步骤与结果是假设阈值T=3时候的结果，同样计算假设阈值为
T=0、T=1、T=2、T=4、T=5的类内方差，比较类内方差之间的值，最小类
内方差使用的阈值T即为图像二值化的阈值。上述是假设图像灰度值级别为
0～5六个值，实际中图像灰度值取值范围为0～255之间，，所以要循环计算
使用每个灰度值作为阈值，得到类内方差，最终取最小类内方差对应的灰度
值作为阈值实现图像二值化即可。
二：代码实现
package com.gloomyfish.filter.study;import java.awt.image.BufferedImage;public class OtsuBinaryFilter extends AbstractBufferedImageOp {public OtsuBinaryFilter(){System.out.println("Otsu Threshold Binary Filter…");}@Overridepublic BufferedImage filter(BufferedImage src, BufferedImage dest) {int width = src.getWidth();int height = src.getHeight();if ( dest == null )dest = createCompatibleDestImage( src, null );// 图像灰度化int[] inPixels = new int[width*height];int[] outPixels = new int[width*height];getRGB( src, 0, 0, width, height, inPixels );int index = 0;for(int row=0; row<height; row++) {int ta = 0, tr = 0, tg = 0, tb = 0;for(int col=0; col<width; col++) {index = row * width + col;ta = (inPixels[index] >> 24) & 0xff;tr = (inPixels[index] >> 16) & 0xff;tg = (inPixels[index] >> 8) & 0xff;tb = inPixels[index] & 0xff;int gray= (int)(0.299 *tr + 0.587*tg + 0.114*tb);inPixels[index] = (ta << 24) | (gray << 16) | (gray << 8) | gray;}}// 获取直方图int[] histogram = new int[256];for(int row=0; row<height; row++) {int tr = 0;for(int col=0; col<width; col++) {index = row * width + col;tr = (inPixels[index] >> 16) & 0xff;histogram[tr]++;}}// 图像二值化 – OTSU 阈值化方法double total = width * height;double[] variances = new double[256];for(int i=0; i<variances.length; i++){double bw = 0;double bmeans = 0;double bvariance = 0;double count = 0;for(int t=0; t<i; t++){count += histogram[t];bmeans += histogram[t] * t;}bw = count / total;bmeans = (count == 0) ? 0 :(bmeans / count);for(int t=0; t<i; t++){bvariance += (Math.pow((t-bmeans),2) * histogram[t]);}bvariance = (count == 0) ? 0 : (bvariance / count);double fw = 0;double fmeans = 0;double fvariance = 0;count = 0;for(int t=i; t<histogram.length; t++){count += histogram[t];fmeans += histogram[t] * t;}fw = count / total;fmeans = (count == 0) ? 0 : (fmeans / count);for(int t=i; t<histogram.length; t++){fvariance += (Math.pow((t-fmeans),2) * histogram[t]);}fvariance = (count == 0) ? 0 : (fvariance / count);variances[i] = bw * bvariance + fw * fvariance;}// find the minimum within class variancedouble min = variances[0];int threshold = 0;for(int m=1; m<variances.length; m++){if(min > variances[m]){threshold = m;min = variances[m];}}// 二值化System.out.println("final threshold value : " + threshold);for(int row=0; row<height; row++) {for(int col=0; col<width; col++) {index = row * width + col;int gray = (inPixels[index] >> 8) & 0xff;if(gray > threshold){gray = 255;outPixels[index] = (0xff << 24) | (gray << 16) | (gray << 8) | gray;}else{gray = 0;outPixels[index] = (0xff << 24) | (gray << 16) | (gray << 8) | gray;}}}setRGB(dest, 0, 0, width, height, outPixels );return dest;}}
运行效果图：
转载文章请注明，博主在此祝各位2015年工作顺利，请继续关注！
怕走崎岖路，莫想登高峰。
