SIFT+RANSAC图像配准matlab代码，投影变换，棋盘格显示配准结果

function [results, options] = RANSAC(X, options) % [results, options] = RANSAC(X, options) % % DESC: % estimate the vector of parameters Theta using RANSAC (see source [1], % [2]) % % VERSION: % 1.1.3 % % INPUT: % % X = input data. The data id provided as a matrix that has % dimesnsions 2dxN where d is the data dimensionality % and N is the number of elements % % options = structure containing the following fields: % % sigma = noise std % P_inlier = Chi squared probability threshold for inliers % (i.e. the probability that an point whose squared % error is less than T_noise_squared is an inlier) % (default = 0.99) % T_noise_squared = Error threshold (overrides sigma) % epsilon = False Alarm Rate (i.e. the probability we never % pick a good minimal sample set) (default = 1e-3) % Ps = sampling probability ( 1 x size(X, 2) ) % (default: uniform, i.e. Ps is empty) % ind_tabu = logical array indicating the elements that should % not be considered to construct the MSS (default % is empty) % est_fun = function that estimates Theta. % Should be in the form of: % % Theta = est_fun(X) % % man_fun = function that returns the residual error. % Should be in the form of: % % [E T_noise_squared] = man_fun(Theta, X) % mode = algorithm variation % 'RANSAC' -> Fischler & Bolles % 'MSAC' -> Torr & Zisserman % % % max_iters = maximum number of iterations (default = inf) % min_iters = minimum number of iterations (default = 0) % max_no_updates = maximum number of iterations with no updates % (default = inf) % fix_seed = true to fix the seed of the random number % generator so that the results on the same data % set are repeatable (default = false) % verbose = true for verbose output % (default = true) % notify_iters = if verbose output is on then print some % information every notify_iters iterations. % If empty information is displayed only for % updates (default = []) % % OUTPUT: % % results = structure containing the following fields: % % Theta = estimated parameter vector % E = fitting error obtained from man_fun % CS = consensus set (true -> inliers, false -> outliers) % J = cost of the solution % iter = number of iterations % % options = options (including the defaults set by the algorithm) % % SEE ALSO: SetPathLocal % AUTHOR: % Marco Zuliani, email: marco.zuliani@gmail.com % Copyright (C) 2008 by Marco Zuliani % % LICENSE: % This toolbox is distributed under the terms of the GNU LGPL. % Please refer to the files COPYING and COPYING.LESSER for more information. % HISTORY: % % 1.1.0 - 01/12/08 - New packaging and some updates % 1.1.1 - 01/17/08 - Fixed a bug to set the max muber of iterations % - Added notify option % 1.1.2 - 04/11/08 - Fixed a couple of minor bugs. % - Initialization and check on the cardinality of % the MSS % 1.1.3 - 06/25/08 - Changed the interface for est_fun and man_fun % so that the sets are specified as arguments % - Added ind_tabu option % - Added optional random seed fixation % - stabilization procedure (beta version) % REFERENCES: % % [1] @ARTICLE{Fischler81, % AUTHOR = "M. A. Fischler and R. C. Bolles", % TITLE = "Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography", % JOURNAL = "Communications of the ACM", % YEAR = "1981", % volume = "24", % pages = "381--395", % } % % [2] @ARTICLE{Torr00, % AUTHOR = {P.H.S. Torr and A. Zisserman}, % TITLE = {{MLESAC}: A New Robust Estimator with Application to Estimating Image Geometry}, % JOURNAL = {Journal of Computer Vision and Image Understanding}, % YEAR = {2000}, % volume = {78}, % number = {1}, % pages = {138�-156}, % } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Check input parameters %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % get the parameters if ~isfield(options, 'stabilize') options.stabilize = false; end; if ~isfield(options, 'sigma') error('The option field sigma must be specified by the user'); end; if ~isfield(options, 'est_fun') error('Estimation function not specified'); end; if ~isfield(options, 'man_fun') error('Manifold function not specified'); end; if ~isfield(options, 'ind_tabu') options.ind_tabu = []; end; if ~isfield(options, 'epsilon') options.epsilon = 1e-3; end; if ~isfield(options, 'mode') options.mode = 'MSAC'; end; if ~isfield(options, 'P_inlier') options.P_inlier = 0.99; end; if ~isfield(options, 'max_iters') options.max_iters = inf; end; if ~isfield(options, 'min_iters') options.min_iters = 0; end; if ~isfield(options, 'max_no_updates') options.max_no_updates = inf; end; if ~isfield(options, 'fix_seed') options.fix_seed = false; end; if ~isfield(options, 'verbose') options.verbose = true; end; if ~isfield(options, 'notify_iters') options.notify_iters = []; end; if ~isfield(options, 'Ps') options.Ps = []; end; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Initializations %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% if (options.verbose) fprintf('\nStarting RANSAC'); if (options.fix_seed) fprintf(' [random number generator seed is fixed]'); end; end; % start timer if (options.verbose), tic; end; % get minimal subset size and model codimension [dummy, k] = feval(options.est_fun, []); if (options.verbose) fprintf('\nMinimal sample set dimension = %d', k); end; % total number of elements N = size(X, 2); % exit if the number of points is smaller than the cardinality of a MSS if (N < k) results.Theta = []; results.E = []; results.CS = []; results.iter = 0; warning('RANSAC:dataSetTooSmall', 'The input data set is composed by too few elements.') return; end % calculate the probability for the inlier detection. if ~isfield(options, 'T_noise_squared') % get the noise threshold via Chi squared distribution [dummy T_noise_squared] = feval(options.man_fun, [], [], options.sigma, options.P_inlier); options.T_noise_squared = T_noise_squared; if (options.verbose) fprintf('\nSquared noise threshold = %f, (assuming Gaussian noise, for sigma = %f)', T_noise_squared, options.sigma); end; else % set a hard noise threshold T_noise_squared = options.T_noise_squared; if (options.verbose) fprintf('\nSquared noise threshold = %f', T_noise_squared); end; end; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Randomization %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% if ~(options.fix_seed) rand('twister',sum(100*clock)); end; global RANSAC_TWISTER_STATE; global RANSAC_TWISTER_STATE_UPDATED; if (options.fix_seed) RANSAC_TWISTER_STATE_UPDATED = RANSAC_TWISTER_STATE; else RANSAC_TWISTER_STAT