Home > Source > Optimise > Model_Based > optimise_warps_model.m

optimise_warps_model

PURPOSE ^

OPTIMISE_WAPRS_MODEL:

SYNOPSIS ^

function [param_list, warped_points, warped_image, score] = optimise_warps_model(image_set, points_set, unwarped_image, unwarped_points, spline_type, placement_method, n_points, verbose, do_plot, subplot_fig, pane, column, image_width, number_of_images, verbose_score, model_evaluation_method, n_modes, weighting_normalisation_method, shape_weight, variation_kept, show_warps_while_optimising, precision_required)

DESCRIPTION ^

 OPTIMISE_WAPRS_MODEL: 

 Code written by Katherine Smith, 2003

    GENERAL

      [param_list, warped_points, warped_image, score] = 
         optimise_warps_model(image_set, points_set, unwarped_image,
         unwarped_points, spline_type, placement_method, n_points,
         verbose, do_plot, subplot_fig, pane,
         image_width, number_of_images, verbose_score)

    INPUT/S

      -X:
          X
           
    OUTPUT/S

      -X:
           X

    PENDING WORK
      
      

    KNOWN BUG/S

      

    COMMENT/S

      

    RELATED FUNCTION/S

      OPTIMISE_WARPS...

    ABOUT

      -Created:     November 23rd, 2003
      -Last update: January 23rd, 2004
      -Revision:    0.1.5
      -Author:      R. S. Schestowitz, University of Manchester
 ==============================================================

CROSS-REFERENCE INFORMATION ^

This function calls: This function is called by:

SOURCE CODE ^

0001 function [param_list, warped_points, warped_image, score] = optimise_warps_model(image_set, points_set, unwarped_image, unwarped_points, spline_type, placement_method, n_points, verbose, do_plot, subplot_fig, pane, column, image_width, number_of_images, verbose_score, model_evaluation_method, n_modes, weighting_normalisation_method, shape_weight, variation_kept, show_warps_while_optimising, precision_required)
0002 % OPTIMISE_WAPRS_MODEL:
0003 %
0004 % Code written by Katherine Smith, 2003
0005 %
0006 %    GENERAL
0007 %
0008 %      [param_list, warped_points, warped_image, score] =
0009 %         optimise_warps_model(image_set, points_set, unwarped_image,
0010 %         unwarped_points, spline_type, placement_method, n_points,
0011 %         verbose, do_plot, subplot_fig, pane,
0012 %         image_width, number_of_images, verbose_score)
0013 %
0014 %    INPUT/S
0015 %
0016 %      -X:
0017 %          X
0018 %
0019 %    OUTPUT/S
0020 %
0021 %      -X:
0022 %           X
0023 %
0024 %    PENDING WORK
0025 %
0026 %
0027 %
0028 %    KNOWN BUG/S
0029 %
0030 %
0031 %
0032 %    COMMENT/S
0033 %
0034 %
0035 %
0036 %    RELATED FUNCTION/S
0037 %
0038 %      OPTIMISE_WARPS...
0039 %
0040 %    ABOUT
0041 %
0042 %      -Created:     November 23rd, 2003
0043 %      -Last update: January 23rd, 2004
0044 %      -Revision:    0.1.5
0045 %      -Author:      R. S. Schestowitz, University of Manchester
0046 % ==============================================================
0047 
0048 % optimise the warps model. Similar to <optimise_warps>
0049       
0050 [grid, steps] = setup_grid(2, unwarped_image, unwarped_points, placement_method, n_points);
0051 
0052 if (do_plot),
0053   figure(subplot_fig);
0054   subplot(number_of_images, column, pane, 'replace');
0055   plot(unwarped_image);
0056   axis([0 image_width 0 1]);
0057   drawnow;
0058 end
0059 
0060 start_points = unwarped_points;
0061 
0062 param_list = [];
0063 start_image = unwarped_image;
0064 %last_score = msd(ref_image, unwarped_image);
0065 if(strcmp(spline_type,'single_point')),
0066   last_score = eval_model_cps_warp([0],grid(1),steps(1), unwarped_image, unwarped_points, image_set, points_set, n_modes, start_points, model_evaluation_method, weighting_normalisation_method, shape_weight, variation_kept, show_warps_while_optimising);
0067   for i=1:size(grid)
0068     [params, score] = fminsearch(@eval_model_cps_warp, [0], optimset('Display', verbose, 'TolX', precision_required), grid(i),steps(i), unwarped_image, unwarped_points, image_set, points_set, n_modes, start_points, model_evaluation_method, weighting_normalisation_method, shape_weight, variation_kept, show_warps_while_optimising);
0069     param_list(size(param_list,1) + 1,:).d = params * steps(i);
0070     param_list(size(param_list,1),:).r = steps(i);
0071     param_list(size(param_list,1),:).centre = grid(i);
0072     start_points = cps_warp_1d(start_points, grid(i), steps(i), params);
0073     start_image = interp1(unwarped_points,unwarped_image,start_points, 'linear',0);
0074   end
0075   warped_points = start_points;
0076   warped_image = start_image;
0077 elseif(strcmp(spline_type,'multi_point')),
0078   warped_grid = grid;
0079   warp_params.green = 'biharmCPS';
0080   last_score = eval_model_multi_warp(zeros(size(grid)),grid,unwarped_image, unwarped_points, image_set, points_set, n_modes, start_points, warp_params, model_evaluation_method, weighting_normalisation_method, shape_weight, variation_kept, show_warps_while_optimising);
0081   [params,score, data] = fminsearch(@eval_model_multi_warp,zeros(size(grid)),optimset('Display', verbose, 'TolX', precision_required),warped_grid,unwarped_image, unwarped_points, image_set, points_set, n_modes, start_points, warp_params, model_evaluation_method, weighting_normalisation_method, shape_weight, variation_kept, show_warps_while_optimising);
0082   param_list(size(param_list,1) + 1,:).d = params;
0083   warped_grid = grid + params;
0084   warped_points = nrr_trans_1d(start_points, grid, warped_grid, warp_params,[]);
0085   warped_image = interp1(unwarped_points,unwarped_image,warped_points, 'linear',0);
0086 else
0087   error('Unknown spline type');    
0088 end
0089 
0090 if (verbose_score == 1),
0091        disp(['Initial score: ', num2str(last_score), ' Final score: ', num2str(score), ' Difference: ', num2str(last_score - score)]);
0092 end
0093 
0094 if (do_plot),
0095   figure(subplot_fig);
0096   subplot(number_of_images, column ,pane,'replace');
0097   plot(warped_image);
0098   axis([0 image_width 0 1]);
0099   title(num2str(score));
0100   drawnow;       
0101 end

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