Hough Transform MATLAB Model - Hough Transform MATLAB Model - 2026.1 English - XD100

Vitis Tutorials: AI Engine Development (XD100)
Document ID
XD100
Release Date
2026-06-23
Version
2026.1 English

You need a proper algorithm model for system partitioning. Begin with the MATLAB® model shown in the following example. A detailed study of this model identifies key aspects of the system design that impact its solution. For example:

  • The image size drives the overall compute load complexity through \(R\) and \(C\) dimensions.

  • The resolution adopted for \(\theta\) through the theta_res parameter drives complexity, bandwidth, and histogram storage.

  • The algorithm exhibits a “dual-nested for-loop” character.

  • The algorithm employs lookup tables through cos_theta and sin_theta.

  • The algorithm is quantized to use int16 data types.

  • There are multiple compute workloads: for rho_i, addr and histogram update H.

function [H,theta,rho,rho_calc] = hough_model( BW, theta_res )
   if     (nargin == 1) theta_res = 180;
   elseif (nargin ~= 2) error('hough_model(BW,theta_res)'); end
   [R,C] = size(BW);
   rho_max = ceil(sqrt((R-1)^2 + (C-1)^2));
   fprintf(1,'rho_max: %g\n',rho_max);
   % Initialize:
   tmp = linspace(-90,90,1+theta_res);
   theta = tmp(1:end-1);
   cos_theta = double(fi(cos(theta*pi/180),1,16,15,'RoundingMethod','Round','OverflowAction','Saturate'));
   sin_theta = double(fi(sin(theta*pi/180),1,16,15,'RoundingMethod','Round','OverflowAction','Saturate'));
   rho = [-rho_max:1:rho_max];
   Nt = numel(theta);
   Nr = numel(rho);
   H = zeros(Nr,Nt);
   rho_calc = zeros(R,C,Nt);
   % Compute transform:
   for yy = 0 : R-1
     for xx = 0 : C-1
       % Compute the 'rho' value:
       rho_i = double(fi(xx*cos_theta + yy*sin_theta,...
                         1,16,0,'RoundingMethod','Round','OverflowAction','Saturate'));
       rho_calc(1+yy,1+xx,:) = rho_i;
       % Add offset to turn onto 'rho' address:
       addr = rho_i + rho_max;
       for ii = 1 : Nt
         H(1+addr(ii),ii) = H(1+addr(ii),ii) + BW(1+yy,1+xx);
     end % yy
   end %xx
end

Run the MATLAB model and compare its performance to the built-in MATLAB hough function from the Image Processing Toolbox. Run it with a \(216\times 240\) image of the AMD Instinct. View heat maps of the 2D Hough Transform output histograms for both the MATLAB function and the AMD 16-bit MATLAB model.

figure