How to find "rectangular" corners?
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Mor
el 23 de Mayo de 2014
Respondida: Matt J
el 6 de Feb. de 2020
Hey,
I have this image:
I want to find the 4 corners of the "rectangular", but I don't want to use the "corner" function. What can I do?
Thanks.
1 comentario
Cedric
el 24 de Mayo de 2014
If your rectangles are not too degenerate, you could get corners with four 2D convolutions using appropriate kernels.
Respuesta aceptada
Matt J
el 24 de Mayo de 2014
Editada: Matt J
el 24 de Mayo de 2014
If the quadrilateral is roughly aligned with the edges of the image, you could also find the corners as follows,
[I,J]=find(Image>max(Image(:))/2);
IJ=[I,J];
[~,idx]=min(IJ*[1 1; -1 -1; 1 -1; -1 1].');
corners=IJ(idx,:)
13 comentarios
Matt J
el 22 de Oct. de 2019
Editada: Matt J
el 22 de Oct. de 2019
Here is a generalization of the approach to arbitrary convex quadrilaterals. No particular orientation is assumed.
N=360;
theta=linspace(0,360,N);
[I,J]=find(Image);
IJ=[I,J];
c=nan(size(theta));
for i=1:N
[~,c(i)]=max(IJ*[cosd(theta(i));sind(theta(i))]);
end
H=histcounts(c,1:numel(I)+1);
[~,k] = maxk(H,4);
corners=IJ(k,:)
Más respuestas (4)
Image Analyst
el 23 de Mayo de 2014
Editada: Image Analyst
el 23 de Mayo de 2014
Why not try the corner() function in the Image Processing Toolbox. What do you have against using that?
Or else call bwboundaries() and go along the coordinates looking for kinks in the curve as shown by the FAQ: http://matlab.wikia.com/wiki/FAQ#How_do_I_find_.22kinks.22_in_a_curve.3F
Matt J
el 24 de Mayo de 2014
Maybe use edge() followed by houghlines() with an appropriate FillGap selection? The endpoints of the line segments returned by houghlines would be the corners of the quadrilateral.
3 comentarios
Image Analyst
el 24 de Mayo de 2014
If you need the exact corner, use (untested)
boundaries = bwboundaries(binaryImage);
x = boundaries{:,1};
y = boundaries{:,2};
and compare every x and y to see which is closest to the hough points
distances = sqrt((xh - x).^ 2 + (yh - y) .^ 2)
[minDistance, indexOfMin] = min(distances);
xc = x(indexOfMin);
yc = y(indexOfMin);
Do the above for each hough estimated point to find the point in the blob which is closest to the hough point (xh, yh).
Matt J
el 24 de Mayo de 2014
Editada: Matt J
el 24 de Mayo de 2014
You can be generous with the RhoResolution, given the large size of the quadrilateral. I get a pretty good fit to the edges with the following,
[H,T,R] = hough(E,'RhoResolution',4,'Theta',-90:.5:89);
Similar to what ImageAnalyst was saying, this initial line fit should allow you to segment the boundary points into 4 separate edges. You do this by finding the closest point to each initial line. You can then do a more refined line fit to each edge using each group of points (e.g., using polyfit).
Image Analyst
el 24 de Mayo de 2014
If the quad is roughly aligned with the edges of the image you could also get the distance from the 4 corners. For each corner, take the one point on the white boundary that has the minimum distance. No need to mess with hough in that case.
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