www.pudn.com > SURF-based-image-stitching.rar > ransac1.m, change:2014-05-10,size:1945b

function [f inlierIdx] = ransac1( x,y,ransacCoef,funcFindF,funcDist ) 
%[f inlierIdx] = ransac1( x,y,ransacCoef,funcFindF,funcDist ) 
%	Use RANdom SAmple Consensus to find a fit from X to Y. 
%	X is M*n matrix including n points with dim M, Y is N*n; 
%	The fit, f, and the indices of inliers, are returned. 
%	RANSACCOEF is a struct with following fields: 
%	minPtNum,iterNum,thDist,thInlrRatio 
%	MINPTNUM is the minimum number of points with whom can we  
%	find a fit. For line fitting, it's 2. For homography, it's 4. 
%	ITERNUM is the number of iteration, THDIST is the inlier  
%	distance threshold and ROUND(THINLRRATIO*n) is the inlier number threshold. 
%	FUNCFINDF is a func handle, f1 = funcFindF(x1,y1) 
%	x1 is M*n1 and y1 is N*n1, n1 >= ransacCoef.minPtNum 
%	f1 can be of any type. 
%	FUNCDIST is a func handle, d = funcDist(f,x1,y1) 
%	It uses f returned by FUNCFINDF, and return the distance 
%	between f and the points, d is 1*n1. 
%	For line fitting, it should calculate the dist between the line and the 
%	points [x1;y1]; for homography, it should project x1 to y2 then 
%	calculate the dist between y1 and y2. 
minPtNum = ransacCoef.minPtNum; 
iterNum = ransacCoef.iterNum; 
thInlrRatio = ransacCoef.thInlrRatio; 
thDist = ransacCoef.thDist; 
ptNum = size(x,2); 
thInlr = round(thInlrRatio*ptNum); 
inlrNum = zeros(1,iterNum); 
fLib = cell(1,iterNum); 
for p = 1:iterNum 
	% 1. fit using  random points 
	sampleIdx = randIndex(ptNum,minPtNum); 
	f1 = funcFindF(x(:,sampleIdx),y(:,sampleIdx)); 
	% 2. count the inliers, if more than thInlr, refit; else iterate 
	dist = funcDist(f1,x,y); 
	inlier1 = find(dist < thDist); 
	inlrNum(p) = length(inlier1); 
	if length(inlier1) < thInlr, continue; end 
	fLib{p} = funcFindF(x(:,inlier1),y(:,inlier1)); 
% 3. choose the coef with the most inliers 
[~,idx] = max(inlrNum); 
f = fLib{idx}; 
dist = funcDist(f,x,y); 
inlierIdx = find(dist < thDist);