function?e=canny_edge（Isigma）?
%functione=edge（I‘canny‘threshsigma）;
%該函數(shù)實(shí)現(xiàn)Canny算子提取邊緣點(diǎn)
%輸入圖像為I，標(biāo)準(zhǔn)差sigma，輸出為邊緣圖像e
I=255*double（I）;%將圖像轉(zhuǎn)化為double型
[mn]=size（I）;
Rr=2:m-1;cc=2:n-1;
e=repmat（logical（uint8（0））mn）;
%產(chǎn)生同樣大小的邊緣圖像e，初始化為1?，即初始化邊緣
GaussianDieOff=0.0001;%設(shè)定高斯函數(shù)消失門限
PercentOfPixelsNotEdges=0.7;%用于計(jì)算邊緣門限
ThresholdRatio=0.2;%設(shè)置兩個(gè)門限的比例
%首先設(shè)計(jì)高斯濾波器和它的微分
pw=1:30;
%設(shè)定濾波器寬度
ssq=sigma*sigma;
%計(jì)算方差
??width?=?find（exp（-（pw.*pw）/（2*ssq））>GaussianDieOff1‘last‘）;
??if?isempty（width）
????width?=?1;??%當(dāng)使用者鍵入很小的sigma時(shí)
??end
%計(jì)算濾波算子寬度
?t?=?（-width:width）;
??gau?=?exp（-（t.*t）/（2*ssq））/（2*pi*ssq）;?????%?一維高斯濾波器
?[xy]=meshgrid（-width:width-width:width）;
??dgau2D=-x.*exp（-（x.*x+y.*y）/（2*ssq））/（pi*ssq）;%高斯濾波器的一階導(dǎo)數(shù)
??%平滑圖像
??aSmooth=imfilter（Igau‘conv‘‘replicate‘）;???%?按行濾波
??aSmooth=imfilter（aSmoothgau‘‘conv‘‘replicate‘）;?%?再按列濾波
??ax?=?imfilter（aSmooth?dgau2D?‘conv‘‘replicate‘）;
??ay?=?imfilter（aSmooth?dgau2D‘?‘conv‘‘replicate‘）;
??mag=sqrt（（ax.*ax）+（ay.*ay））;
%計(jì)算濾波結(jié)果的幅度
magmax=max（mag（:））;
if?magmax>0
mag=mag/magmax;
%對濾波幅度進(jìn)行歸一化
end
%下面根據(jù)濾波幅度的概率密度計(jì)算濾波門限
[countsx]=imhist（mag64）;
%計(jì)算濾波結(jié)果的幅度的直方圖
highThresh=min（find（cumsum（counts）>PercentOfPixelsNotEdges*m*n））/64;
%通過設(shè)定非邊緣點(diǎn)的比例來確定高門限
lowThresh=ThresholdRatio*highThresh;
%設(shè)置低門限為高門限乘以比例因子
thresh=[lowThreshhighThresh];
%下面進(jìn)行非極大抑制
%大于高門限的點(diǎn)歸于強(qiáng)邊緣圖像
%小于低門限的點(diǎn)歸于弱邊緣圖像
idxStrong=[];
for?dir=1:4
?idxLocalMax=cannyFindLocalMaxima（diraxaymag）;
idxWeak=idxLocalMax（mag（idxLocalMax）>lowThresh）;
e（idxWeak）=1;
idxStrong=[idxStrong;idxWeak（mag（idxWeak）>highThresh）];
end
rstrong=rem（idxStrong-1m）+1;%rem是求余數(shù)
cstrong=floor（（idxStrong-1）/m）+1;%向-∞取整
e=bwselect（ecstrongrstrong8）;????
%通過形態(tài)學(xué)算子將兩幅圖像的邊緣進(jìn)行連接
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
%???Local?Function?:?cannyFindLocalMaxima
%
function?idxLocalMax?=?cannyFindLocalMaxima（directionixiymag）
%
%?This?sub-function?helps?with?the?non-maximum?suppression?in?the?Canny
%?edge?detector.??The?input?parameters?are:
%?
%???direction?-?the?index?of?which?direction?the?gradient?is?pointing?
%???????????????read?from?the?diagram?below.?direction?is?1?2?3?or?4.
%???ix????????-?input?image?filtered?by?derivative?of?gaussian?along?x?
%???iy????????-?input?image?filtered?by?derivative?of?gaussian?along?y
%???mag???????-?the?gradient?magnitude?image
%
%????there?are?4?cases:
%
%?????????????????????????The?X?marks?the?pixel?in?question?and?each
%?????????3?????2?????????of?the?quadrants?for?the?gradient?vector
%???????O----0----0???????fall?into?two?cases?divided?by?the?45?
%?????4?|?????????|?1?????degree?line.??In?one?case?the?gradient
%???????|?????????|???????vector?is?more?horizontal?and?in?the?other
%???????O????X????O???????it?is?more?vertical.??There?are?eight?
%???????|?????????|???????divisions?but?for?the?non-maximum?suppression??
%????（1）|?????????|（4）????we?are?only?worried?about?4?of?them?since?we?
%???????O----O----O???????use?symmetric?po

?屬性????????????大小?????日期????時(shí)間???名稱
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?????文件?????431818??2010-08-21?13:04??canny\3.jpg

?????文件???????4681??2011-03-19?12:47??canny\canny_edge.m

?????目錄??????????0??2011-03-24?14:01??canny

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???????????????436499????????????????????3