基于sift算法的图像拼接

基于sift算法的图像拼接matlab代码，经过测试，可以运行，对有重叠部分的图像进行拼接，效果不错

function [Him1_inliersim2_inliers] = ComputeH（im1_matches im2_matches nb_iter epsilon）
%
% ComputeH
%   -> robust estimation of the homography using RANSAC
%
% function [Him1_inliersim2_inliers] = ComputeH（im1_matches im2_matches nb_iter epsilon）
% 
% Input
%   - im1_matches im2_points : points matching in the two images
%       im2 = Him1
%   - nb_iter : number of iterations for the RANSAC algorithm
% 
% Output
%   - H : computed homography

% @author: JB Fiot
% @Version: Nov08


    nb_points = size（im1_matches1）;
    Best_nb_inliers = 0;

    for iter=1:nb_iter
        selected_points = [];
        im1_inliers=[]; im2_inliers=[];
        while size（unique（selected_points）1）~=4
            selected_points = random（‘discrete uniform‘ nb_points [4 1]）;
        end
        
        A=[];
        
		for i=1:4
			x1 = im1_matches（selected_points（i）1）;
			y1 = im1_matches（selected_points（i）2）;
			x2prime = im2_matches（selected_points（i）1）;
			y2prime = im2_matches（selected_points（i）2）;

			ax = [-x1-y1-1 000 x2prime*x1x2prime*y1x2prime];
			ay = [000 -x1-y1-1 y2prime*x1y2prime*y1y2prime];

            A = [A;ax;ay];
        end
            
 		[USV] = svd（A）;
 		h = V（:end）;
        
        H = reshape（h [33]）‘;
        H = H/H（33）;
        
        nb_inliers = 0;
        for i=1:nb_points
            Hi = H*[im1_matches（i1）;im1_matches（i2）;1];
         %   invHi=inv（H）*[im1_matches（i1）;im1_matches（i2）;1];
          %  if （Hi（3） ~= 0 && invHi（3）~=0）
           %     if （（Hi（1）/Hi（3）-im2_matches（i1））^2+（Hi（2）/Hi（3）-im2_matches（i2））^2+（invHi（1）/invHi（3）-im1_matches（i1））^2+（invHi（2）/invHi（3）-im1_matches（i2））^2）            if Hi（3） ~= 0
                if （（Hi（1）/Hi（3）-im2_matches（i1））^2+（Hi（2）/Hi（3）-im2_matches（i2））^2）                    nb_inliers = nb_inliers + 1;
                    im1_inliers = [im1_inliers;im1_matches（i:）];
                    im2_inliers = [im2_inliers;im2_matches（i:）];
                end
            end
		
        end
        
        if nb_inliers>Best_nb_inliers
            Best_nb_inliers = nb_inliers;
            Best_im1_inliers=im1_inliers;
            Best_im2_inliers=im2_inliers;
        end
              
    end
    im1_inliers=Best_im1_inliers;
    im2_inliers=Best_im2_inliers;
    
    %Re-estimation of the homographies with all inliers.
    A=[];
    for i=1:size（im1_inliers1）
		x1 = im1_inliers（i1）;
        y1 = im1_inliers（i2）;
        x2prime = im2_inliers（i1）;
        y2prime = im2_inliers（i2）;

		ax = [-x1-y1-1 000 x2prime*x1x2prime*y1x2prime];
		ay = [000 -x1-y1-1 y2prime*x1y2prime*y1y2prime];
            
        A = [A;ax;ay];
    end


    [USV] = svd（A）;
    h = V（:end）;
    H = reshape（h [33]）‘;
    t=H（1:）;
    H（1:）=H（2:）;
    H（2:）=t;
    t=H（:1）;
    H（:1）=H（:2）;
    H（:2）=t;
    
    

 属性            大小     日期    时间   名称
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     文件       2990  2009-06-04 23:19  492216156 Image_stitching\acomputeH.m

     文件        846  2009-06-04 23:19  492216156 Image_stitching\adist2.m

     文件       1267  2009-06-04 23:19  492216156 Image_stitching\aGetDescriptors.m

     文件       1171  2009-06-04 23:19  492216156 Image_stitching\aharris.m

     文件        642  2009-06-04 23:19  492216156 Image_stitching\aLessHarrisPoints.m

     文件       1306  2009-06-04 23:19  492216156 Image_stitching\aMatchDescriptors.m

     文件       5762  2009-06-04 23:19  492216156 Image_stitching\aStitcher.m

     文件       3001  2009-06-04 23:19  492216156 Image_stitching\avgg_warp_H.m

     文件         30  2013-04-25 11:07  492216156 Image_stitching\read.txt

     目录          0  2013-04-25 11:06  492216156 Image_stitching\data

     目录          0  2013-04-25 11:07  492216156 Image_stitching

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