[cv]edge detection: 2d operators
2017-05-09 00:30
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derivative of 2d gaussian filter
Gaussian derivative in x,y direction
effect of sigma in smoothing filter
canny edge operator
in matlab, we can use command: edge(image, ‘canny’) easily.
In canny operator, thinning steps can be called non-maximal suppression.
How to link the line segments.
% For Your Eyes Only pkg load image; frizzy = imread('frizzy.png'); froomer = imread('froomer.png'); imshow(frizzy); imshow(froomer); gray_frizzy = rgb2gray(frizzy); gray_froomer = rgb2gray(froomer); % TODO: Find edges in frizzy and froomer images frizzy_edges = edge(gray_frizzy,'canny'); froomer_edges = edge(gray_froomer, 'canny'); % TODO: Display common edge pixels img = frizzy_edges | froomer_edges; imshow(img);
matlab edge function help
edge - Find edges in intensity image
This MATLAB function returns a binary image BW containing 1s where the function
finds edges in the input image I and 0s elsewhere.
BW = edge(I)
BW = edge(I,’Sobel’)
BW = edge(I,’Sobel’,threshold)
BW = edge(I,’Sobel’,threshold,direction)
BW = edge(I,’Sobel’,threshold,direction,’nothinning’)
[BW,threshOut] = edge(I,’Sobel’,_)
BW = edge(I,’Prewitt’)
BW = edge(I,’Prewitt’,threshold)
BW = edge(I,’Prewitt’,threshold,direction)
BW = edge(I,’Prewitt’,threshold,direction,’nothinning’)
[BW,threshOut] = edge(I,’Prewitt’,_)
BW = edge(I,’Roberts’)
BW = edge(I,’Roberts’,threshold)
BW = edge(I,’Roberts’,threshold,’nothinning’)
[BW,threshOut] = edge(I,’Roberts’,threshold,’nothinning’)
BW = edge(I,’log’)
BW = edge(I,’log’,threshold)
BW = edge(I,’log’,threshold,sigma)
[BW,threshOut] = edge(I,’log’,_)
BW = edge(I,’zerocross’,threshold,h)
[BW,threshOut] = edge(I,’zerocross’,_)
BW = edge(I,’Canny’)
BW = edge(I,’Canny’,threshold)
BW = edge(I,’Canny’,threshold,sigma)
[BW,threshOut] = edge(I,’Canny’,_)
BW = edge(I,’approxcanny’)
BW = edge(I,’approxcanny’,threshold)
[gpuarrayBW,threshOut] = edge(gpuarrayI,_)
另请参阅 fspecial, gpuArray, imgradient, imgradientxy
edge 的参考页
名为 edge 的其他函数
sigma effect in Canny Edge Detection
single 2d edge detection filter
>> lena = imread('../pics/lena512color.tiff'); >> figure,imshow(lena),title('original image,colorful'); >> lenagray = rgb2gray(lena); >> figure,imshow(lenagray),title('original image,gray'); >> h = fspecial('gaussian', [11,11],4); >> figure,surf(h); >> lenaSmooth = imfilter(lenagray,h); >> figure,imshow(lenaSmooth); >> % method 01: shift left and right, show difference >> lenaL = lenagray; >> lenaL(:,[1:end-1])=lenaL(:,[2:end]); >> lenaL = lenaSmooth; >> lenaL(:,[1:end-1])=lenaL(:,[2:end]); >> lenaR = lenaSmooth; >> lenaR(:,[2:end]) = lenaR(:,[1:end-1]); >> lenaDiff = double(lenaR) - double(lenaL); >> figure, imshow(lenaDiff,[]),title('difference between right and left shifted images');% because there exist negetive value use imshow(img,[]) >> %% method 2: Canny edge detector >> cannyGray = edge(lenagray,'canny'); >> figure, imshow(cannyGray),title('edges in origin image'); >> cannySmooth = edge(lenaSmooth,'canny'); >> figure, imshow(cannySmooth),title('edges in smoothed image'); >> %% method 03: laplacian of gaussian >> cannyLog = edge(lenagray,'log'); >> figure, imshow(cannyLog),title('edges in laplaced image'); >>
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