Circular Hough Transform.

 

function call:

[out_img1]=vsg('CircHough',in_img1,num1,num2,num3);

Arguments:

in_img1 input image, 3 channel RGB, 1 channel greyscale or binary image.

num1 – an integer representing the smallest circle to look for in the input image.

num2 – an integer representing the largest circle to look for in the input image.

num3 – an integer representing the size of the radius increment between num1 and num2.

 

Description:

The CircHough function applies the generalized Hough Transform (HT) to detect circles from a binary image.

out_img1 – An image displaying the Circular Hough transform of the input image. The dimensions are the same as the input image dimension in the x and y direction, and will have one slice for each size of circle to be found, i.e. (num2-num1)/num3;

 

Example:

in_img1=openimage('circle.jpg');

in_img1=255*(in_img1>128);

h=figure, image(uint8(in_img1)); set(h,'Name','Input image');

tic;[out_img1]=vsg('CircHough',in_img1,10,100,5);toc

[out_img2]=vsg('Threshold',out_img1,0.90*max(max(max(out_img1))));

[out_img3]=vsg('InvCircHough',out_img2,10,100,5);

[out_img4]=vsg('Add',in_img1,out_img3);

h=figure, image(uint8(out_img3)); set(h,'Name','Inv circle hough image');

h=figure, image(uint8(out_img4));set(h,'Name','Combined image');

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