Sobel、Prewitt、Roberts算子的邊緣檢測

1、實驗 3 邊緣檢測一、實驗?zāi)康?、掌握差分算法（1 階和2階）2、理解 canny 算子二、實驗內(nèi)容使用Matlab或者VC實現(xiàn)下面幾個內(nèi)容1、（一階差分）實現(xiàn)分別采用 Sobel、Prewitt、Roberts 算子的邊緣檢測?？梢圆捎?公式法，也可以采用模板法。2、（二階差分）實現(xiàn)分用zerocross （也稱過零檢測、Laplacian）邊緣檢測3、實現(xiàn)圖像卷積的空域與頻域算法4、利用 Matlab 實現(xiàn) canny 算子-110010-10100-101-10-10-110010-10100-101-10-101-101-101111000-1-1-1Sobel121000Sobel1

2、21000-1-2-1Laplacian111181111Canny三、 實驗程序1.(一階差分)% Sobel 算子clear;sourcePic=imread(111.jpg);%讀 取原圖像 grayPic=mat2gray(sourcePic);%實現(xiàn)圖像矩陣的歸一化操作 m,n=size(grayPic);newGrayPic=grayPic;%為保留圖像的邊緣一個像素 sobelNum=0;%經(jīng)sobel算子計算得到的每個像素的值 sobelThreshold=0.8;% 設(shè)定閾值for j=2:m-1 %進(jìn)行邊界提取for k=2:n-1 sobelNum=abs(grayPic

3、(j-1,k+1)+2*grayPic(j,k+1)+grayPic(j+1,k+1)- .,grayPic(j-1,k-1)-2*grayPic(j,k-1)-grayPic(j+1,k-1)+abs( ., grayPic(j-1,k-1)+2*grayPic(j-1,k)+grayPic(j-1,k+1)-grayPic(j+1,k-1) ., -2*grayPic(j+1,k)-grayPic(j+1,k+1);if(sobelNum sobelThreshold)newGrayPic(j,k)=255;elsenewGrayPic(j,k)=0;endendendfigure,ims

4、how(newGrayPic);title(Sobel 算子的處理結(jié)果)% Prewitt 算子clear;sourcePic=imread(111.jpg);%讀 取原圖像 grayPic=mat2gray(sourcePic);%實現(xiàn)圖像矩陣的歸一化操作 m,n=size(grayPic);newGrayPic=grayPic;%為保留圖像的邊緣一個像素PrewittNum=0;%經(jīng)Prewitt算子計算得到的每個像素的值PrewittThreshold=0.5;% 設(shè) 定閾值for j=2:m-1 %進(jìn)行邊界提取for k=2:n-1PrewittNum=abs(grayPic(j-1,

5、k+1)-grayPic(j+1,k+1)+grayPic(j-1,k) ., -grayPic(j+1,k)+grayPic(j-1,k-1)-grayPic(j+1,k-1)+abs .,(grayPic(j-1,k+1)+grayPic(j,k+1)+grayPic(j+1,k+1)-grayPic(j-1,k-1) ., -grayPic(j,k-1)-grayPic(j+1,k-1);if(PrewittNum PrewittThreshold)newGrayPic(j,k)=255;elsenewGrayPic(j,k)=0;endendendfigure,imshow(newGr

6、ayPic);title(Prewitt算子的處理結(jié)果)% Roberts 算子 clear;sourcePic=imread(111.jpg); %讀取原圖像grayPic=mat2gray(sourcePic); %實現(xiàn)圖像矩陣的歸一化操作 m,n=size(grayPic);newGrayPic=grayPic;%為保留圖像的邊緣一個像素robertsNum=0; %經(jīng) roberts 算子計算得到的每個像素的值robertThreshold=0.2; %設(shè)定閾值for j=1:m-1 %進(jìn)行邊界提取for k=1:n-1robertsNum = abs(grayPic(j,k)-gra

7、yPic(j+1,k+1) + abs(grayPic(j+1,k)-grayPic(j,k+1); if(robertsNum robertThreshold)newGrayPic(j,k)=255;elsenewGrayPic(j,k)=0;endendend figure,imshow(newGrayPic);title(roberts 算子的處理結(jié)果)2.(2階算子)I=imread(111.jpg); %讀取圖像I1=im2double(I); %將彩圖序列變成雙精度I2=rgb2gray(I1); %將彩色圖變成灰色圖thr, sorh, keepapp=ddencmp(den,w

8、v,I2);I3=wdencmp(gbl,I2,sym4,2,thr,sorh,keepapp); %小波除噪I4=medfilt2(I3,9 9); %中值濾波I5=imresize(I4,0.8,bicubic); %圖像大小h=fspecial(gaussian,5); %高斯濾波BW=edge(I5,zerocross, ,h); %zerocross 圖像邊緣提取figure;imshow(BW);title(Zerocross);3. 圖像卷積的空域與時域算法inimg = imread(cameraman.tif);subplot(131)imshow(inimg), title

9、(Original image)M,N = size(inimg);% Original image size%=h = fspecial(gaussian,25,4); % Gaussian filter%=% 空域濾波gx = imfilter(inimg,h,same,replicate); % 空域圖像濾波 subplot(132)imshow(gx,);title(Spatial domain filtering) %= % 頻域濾波%=h_hf = floor(size(h)/2); % 空域濾波器半高/寬imgp = padarray(inimg, h_hf(1),h_hf(2)

10、,replicate); % Padding boundary with copying pixels PQ = 2*size(imgp);Fp = fft2(double(imgp), PQ(1), PQ(2); % 延拓圖像 FFTP = PQ(1); Q = PQ(2);center_h = h_hf+1; % 空域小模板 h 中心位置hp = zeros(PQ);%預(yù)分配內(nèi)存，產(chǎn)生PXQ零矩陣hp(1:size(h,1),1:size(h,2) = h; % h 置于 hp 左上角hp = circshift(hp,-(center_h(1)-1),-(center_h(2)-1);

11、% 循環(huán)移位， h 中心置于 hp 左上角 %=Hp = fft2(double(hp);% hp 濾波器做 FFT%=Gp = Hp.*Fp;% 頻域濾波gp = real(ifft2(Gp);% 反變換，取實部gf = gp(h_hf(1)+1:M+ h_hf(1), h_hf(2)+1:N + h_hf(2); % 截取有效數(shù)據(jù) subplot(133) imshow(uint8(gf),), title(Frequency domain filtering)% 注：以上處理中，頻域圖像 Fp 與濾波器 Hp 均未中心化，因此，返回空域時無需反中心 化。%另外，直接調(diào)用Hp = freqz2(h,PQ)獲得的2D頻域響應(yīng)，則是中心化的。4. Canny 算子I = imread(111.jpg); % 讀入圖像I=rgb2gray(I);% 轉(zhuǎn)化為灰色圖像subplot(121)imshow(l),title (原圖);BW