function detect_object(image,scene,to_plot)
% INPUTS
% image - a square rgb image containing the main object
% scene - a square rgb image containing a set of objects
% The goal of the algorithm is to detect the main object in the scene.

if nargin < 3
to_plot=0;
end

%make the image into a gray image
image=rgb2gray(image);

%Extract the edges. For the image given
%to make a logical image based on if the pixel
%values exceed 160 or not gives very clear edges
%and produces simpler edges than the 'canny' algorithm

edges_image=(image<160);%edge(image,'canny');
figure
imshow(edges_image)
title('Edges in the image');

%This is for plotting information only and shows what the Hough
%transform extracts out of the input image.
%This is standard Matlab sample code for the Hough transform.
if to_plot
[Hi,Ti,Ri]=hough(edges_image);
figure, imshow(imadjust(mat2gray(Hi)),[],'XData',Ti,'YData',Ri,...
         'InitialMagnification','fit');
xlabel('\theta (degrees)'), ylabel('\rho');
axis on, axis normal, hold on;
colormap(hot)
title('image');
Pi = houghpeaks(Hi,15,'threshold',ceil(0.3*max(Hi(:))));
xi = Ti(Pi(:,2));
yi = Ri(Pi(:,1));
hold on
plot(xi,yi,'s','color','black');
lines = houghlines(edges_image,Ti,Ri,Pi,'FillGap',5,'MinLength',7);
figure;
imshow(edges_image);
max_len=0;
hold on
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
end

Vehicle Detection in Static Images using Matlab. The &quot;Neural...

Vehicle Detection in Static Images using Matlab. The "Neural...