# Find square cell centres in a noisy checker board/chess board image using pattern detection

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Harsha K 2019 年 10 月 24 日

Dear all,
I have a checker board/chess board pattern detection problem. I am interested in knowing the centres of the squares in my image frame.
I have tried the following approach:
clc;
close all;
clear;
% Load attached image (Extract the tiff file from the zip file :-) )
% Increase contrast of the image
% Crop the right half of the image for convenience and avoiding the dark
% blob on the left half of the image
I = I(:, 695:end);
% The code below is taken from Matlab help sections on detecting edges and
% examples provided by matlab users
BW = edge(I, 'Canny');
figure;
imshowpair(I,BW,'montage')
% Calculate the hough transform of the edge detected binarised image above.
figure;
[H,T,R] = hough(BW);
imshow(H,[],'XData',T,'YData',R,...
'InitialMagnification','fit');
xlabel('\theta'), ylabel('\rho');
axis on, axis normal, hold on;
% Plot the hough transform peaks by thresholding
P = houghpeaks(H,40,'threshold',ceil(0.3*max(H(:))));
x = T(P(:,2)); y = R(P(:,1));
plot(x,y,'s','color','white');
% Attempt to plot the lines (vertical and horizontal) running between the
% square cells such that cell centres can be extracted.
lines = houghlines(BW,T,R,P,'FillGap',5,'MinLength',7);
figure, imshow(I), hold on
max_len = 0;
for k = 1:length(lines)
xy = [lines(k).point1; lines(k).point2];
plot(xy(:,1),xy(:,2),'LineWidth',2,'Color','green');
% Plot beginnings and ends of lines
plot(xy(1,1),xy(1,2),'x','LineWidth',2,'Color','yellow');
plot(xy(2,1),xy(2,2),'x','LineWidth',2,'Color','red');
% Determine the endpoints of the longest line segment
len = norm(lines(k).point1 - lines(k).point2);
if ( len > max_len)
max_len = len;
xy_long = xy;
end
end
As you can see, this is not the desired result. I would need the complete grid detected. Is there a simple, computationally non-intensive (as a last resort, it is still OK to be laborious task as cell centre detection will only be done once) way out to solve this.

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### 回答 (1 件)

Image Analyst 2019 年 11 月 29 日
What I'd do is to get the sum or mean of the gray level along each direction and look for peaks.
verticalProfile = mean(grayImage, 2);
figure
subplot(2, 1, 1);
plot(verticalProfile, 'b-', 'LineWidth', 2);
grid on;
horizontalProfile = mean(grayImage, 1);
subplot(2, 1, 2);
plot(horizontalProfile, 'b-', 'LineWidth', 2);
grid on;
Then call findpeaks(). Give that a try and report back here to me.

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R2018a

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