實(shí)驗(yàn)四圖像分割與邊緣檢測

1、實(shí)驗(yàn) 四 圖像分割與邊緣檢測 一實(shí)驗(yàn)?zāi)康募耙?利用MATLAB研究圖像分割與邊緣檢測的常用算法原理；2掌握MATLAB圖像域值分割與邊緣檢測函數(shù)的使用方法；3了解邊緣檢測的算法和用途，比較Sobel、Prewitt、Canny等算子邊緣檢測的差異。二、實(shí)驗(yàn)內(nèi)容（一）研究以下程序，分析程序功能；輸入執(zhí)行各命令行，認(rèn)真觀察命令執(zhí)行的結(jié)果。熟悉程序中所使用函數(shù)的調(diào)用方法，改變有關(guān)參數(shù)，觀察試驗(yàn)結(jié)果。1圖像閾值分割clear all, close all;I = imread('cameraman.tif');figure (1),imshow(I)figure(2); imhist

2、(I)T=120/255;Ibw1 = im2bw(I,T); figure(3);subplot(1,2,1), imshow(Ibw1);T=graythresh(I); L = uint8(T*255)Ibw2 = im2bw(I,T); subplot(1,2,2), imshow(Ibw2);help im2bw;help graythresh;clear all, close all;I = imread('cameraman.tif');figure (1),imshow(I)figure(2); imhist(I)T=240/255;Ibw1 = im2bw(I

3、,T); figure(3);subplot(1,2,1), imshow(Ibw1);T=graythresh(I); L = uint8(T*255)Ibw2 = im2bw(I,T); subplot(1,2,2), imshow(Ibw2);help im2bw;help graythresh;clear all, close all;I = imread('cameraman.tif');figure (1),imshow(I)figure(2); imhist(I)T=120/255;Ibw1 = im2bw(I,T); figure(3);subplot(1,2,

4、1), imshow(Ibw1);T=graythresh(I); L = uint8(T*255)Ibw2 = im2bw(I,T); subplot(1,2,2), imshow(Ibw2);help im2bw;help graythresh;2邊緣檢測clear all, close all;I = imread('moon.tif');BW1 = edge(I,'sobel');BW2 = edge(I,'canny');BW3 = edge(I,'prewitt');BW4 = edge(I,'roberts&

5、#39;);BW5 = edge(I,'log');figure(1), imshow(I), title('Original Image');figure(2), imshow(BW1), title('sobel');figure(3), imshow(BW2), title('canny');figure(4), imshow(BW3), title('prewitt');figure(5), imshow(BW4), title('roberts');figure(6), imshow(BW

6、5), title('log');help edgeedgedemo（二）利用MATLAB熟悉并驗(yàn)證其它圖像分割方法灰度閾值分割：I=imread('C:UsersAdministratorDesktoprice.jpg'); I=rgb2gray(I); I2=im2bw(I); figure,imshow(I2); I2=im2bw(I,140/255); figure,imshow(I2) 區(qū)域分割法：I=imread('eight.tif'); imshow(I) c=222 272 300 270 221 194; r=21 21 75

7、 121 121 75; BW=roipoly(I,c,r); figure,imshow(BW) H=fspecial('unsharp'); J1=roifilt2(H,I,BW); figure,imshow(J1) J2=roifill(I,c,r); figure,imshow(J2) 分水嶺分割法：f=imread('C:UsersAdministratorDesktopcell.jpg'); imshow(f); g=im2bw(f, graythresh(f); figure,imshow(g); gc=g; D=bwdist(gc); L=wa

8、tershed(-D); w=L=0; g2=g&w; figure,imshow(g2)（三）采用MATLAB編程實(shí)現(xiàn)自動全局閾值算法，對圖像'rice.tif'進(jìn)行二值化分割算法步驟：1）選取一個的初始估計(jì)值T；2）用T分割圖像。這樣便會生成兩組像素集合：G1由所有灰度值大于 T 的像素組成，而G2由所有灰度值小于或等于 T 的像素組成。3）對G1和G2中所有像素計(jì)算平均灰度值m1和m2。4）計(jì)算新的閾值： T =(m1+m2)/25）重復(fù)步驟（2）到（4），直到逐次迭代所得到的 T 值之差小于一個事先定義的參數(shù)To，即，如果 |Tn Tn-1|<To ，則停

9、止。clc;clear all; I=imread('C:UsersAdministratorDesktoprice.gif');I=double(I)/255; k1=(max(max(I)+min(min(I)/2; rows cols=size(I); count1=0; count2=0; for i=1:rows for j=1:cols if I(i,j)<k1 count1=count1+1; G1(count1).I=I(i,j); else count2=count2+1; G2(count2).I=I(i,j); end end end k2=(mea

10、n(mean(G1.I)+mean(mean(G2.I)/2; while(abs(k2-k1)>(5/255) k1=k2; count1=0; count2=0; for i=1:rows for j=1:cols if I(i,j)<k1 count1=count1+1; G1(count1).I=I(i,j); else count2=count2+1; G2(count2).I=I(i,j); end end end k2=(mean(mean(G1.I)+mean(mean(G2.I)/2; end figure(1);imshow(I); figure(2);II=im2bw(I,k2);imshow(II