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Farthest Point Sampling的原理是,先随机选一个点,然后呢选择离这个点距离最远的点(D中值最大的点)加入起点,然后继续迭代,直到选出需要的个数为止
其主要代码如下:
%main.mclear options;n = 400;[M,W] = load_potential_map('mountain', n);npoints_list = round(linspace(20,200,6));%采样点个数列表landmark = [];options.verb = 0;ms = 15;clf;for i=1:length(npoints_list) nbr_landmarks = npoints_list(i); landmark = perform_farthest_point_sampling( W, landmark, nbr_landmarks-size(landmark,2), options );%nbr_landmarks-size(landmark,2) 减去已经存在的点数 %landmark为已采样的点(包括原来的点和新增的点) % compute the associated triangulation [D,Z,Q] = perform_fast_marching(W, landmark); % display sampling and distance function D = perform_histogram_equalization(D,linspace(0,1,n^2));%把D中的值拉到[0,1]范围内 subplot(2,3,i); hold on; imageplot(D'); plot(landmark(1,:), landmark(2,:), 'r.', 'MarkerSize', ms); title([num2str(nbr_landmarks) ' points']); hold off; colormap jet(256);end
%perform_farthest_point_sampling.mfunction [points,D] = perform_farthest_point_sampling( W, points, npoints, options )% points为已经采样了的点,npoints表示需要加入采样点的个数% perform_farthest_point_sampling - samples points using farthest seeding strategy%% points = perform_farthest_point_sampling( W, points, npoints );%% points can be [] or can be a (2,npts) matrix of already computed % sampling locations.% % Copyright (c) 2005 Gabriel Peyreoptions.null = 0;if nargin<3 npoints = 1;endif nargin<2 points = [];endn = size(W,1);aniso = 0;d = nb_dims(W);if d==4 aniso = 1; d = 2; % tensor fieldelseif d==5 aniso = 1; d = 3; % tensor fieldends = size(W);s = s(1:d);% domain constraints (for shape meshing)L1 = getoptions(options, 'constraint_map', zeros(s) + Inf );verb = getoptions(options, 'verb', 1);mask = not(L1==-Inf);if isempty(points) % initialize farthest points at random points = round(rand(d,1)*(n-1))+1;%随机一个点的d维坐标 % replace by farthest point [points,L] = perform_farthest_point_sampling( W, points, 1 );%然后选点到points最远的距离 Q = ones(size(W)); points = points(:,end);%取最后一个点,即就是生成的离初始随机点最远的那个点 npoints = npoints-1;%需要生成的点数减1else % initial distance map [L,Q] = my_eval_distance(W, points, options);%如果初始已存在一些采样点,则可以通过perform_fast_marching算距离了, points为初始点(距离为0的点)% L = min(zeros(s) + Inf, L1);% Q = zeros(s);endfor i=1:npoints if npoints>5 && verb==1 progressbar(i,npoints); end options.nb_iter_max = Inf; options.Tmax = Inf; % sum(size(W)); % [D,S] = perform_fast_marching(W, points, options); options.constraint_map = L; pts = points; if not(aniso) pts = pts(:,end);%为何只取最一个点?因为前面的距离都算好了,存储在L中 end D = my_eval_distance(W, pts, options); Dold = D; D = min(D,L); % known distance map to lanmarks L = min(D,L1); % cropp with other constraints if not(isempty(Q)) % update Voronoi Q(Dold==D) = size(points,2); end % remove away data D(D==Inf) = 0; if isempty(Q) % compute farthest points [tmp,I] = max(D(:));%找距离最远的点 [a,b,c] = ind2sub(size(W),I(1)); else % compute farthest steiner point [pts,faces] = compute_saddle_points(Q,D,mask); a = pts(1,1); b = pts(2,1); c = [];%第1列,为距离D最大的值 if d==3 c = pts(3,1); end end if d==2 % 2D points = [points,[a;b]]; else % 3D points = [points,[a;b;c]]; end end%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%function [D,Q] = my_eval_distance(W, x, options)%给点权值矩阵W, 初始点x(距离为0的点),则算各点的距离% D is distance% Q is voronoi segmentationoptions.null = 0;n = size(W,1);d = nb_dims(W);if std(W(:))1 D = zeros(n)+Inf; Q = zeros(n); for i=1:size(x,2) Dold = D; Qold = Q; D = min(Dold, my_eval_distance(W,x(:,i))); % update voronoi segmentation Q(:) = i; Q(D==Dold) = Qold(D==Dold); end return; end if d==2 [Y,X] = meshgrid(1:n,1:n); D = 1/W(1) * sqrt( (X-x(1)).^2 + (Y-x(2)).^2 ); else [X,Y,Z] = ndgrid(1:n,1:n,1:n); D = 1/W(1) * sqrt( (X-x(1)).^2 + (Y-x(2)).^2 + (Z-x(3)).^2 ); end Q = D*0+1;else [D,S,Q] = perform_fast_marching(W, x, options);end
%perform_fast_marching.mfunction [D,S,Q] = perform_fast_marching(W, start_points, options)% perform_fast_marching - launch the Fast Marching algorithm, in 2D or 3D.%% [D,S,Q] = perform_fast_marching(W, start_points, options)%% W is an (n,n) (for 2D, d=2) or (n,n,n) (for 3D, d=3) % weight matrix. The geodesics will follow regions where W is large.% W must be > 0.% 'start_points' is a d x k array, start_points(:,i) is the ith starting point .%% D is the distance function to the set of starting points.% S is the final state of the points : -1 for dead (ie the distance% has been computed), 0 for open (ie the distance is only a temporary% value), 1 for far (ie point not already computed). Distance function% for far points is Inf.(注意对于far来说,1是状态,Inf是距离)% (按照书上的说法,-1为known的点,0为trial点,1为far点)% Q is the index of the closest point. Q is set to 0 for far points.% Q provide a Voronoi decomposition of the domain. %% Optional:% - You can provide special conditions for stop in options :% 'options.end_points' : stop when these points are reached% 'options.nb_iter_max' : stop when a given number of iterations is% reached.% - You can provide an heuristic in options.heuristic (typically that try to guess the distance% that remains from a given node to a given target).% This is an array of same size as W.% - You can provide a map L=options.constraint_map that reduce the set of% explored points. Only points with current distance smaller than L% will be expanded. Set some entries of L to -Inf to avoid any% exploration of these points.% - options.values set the initial distance value for starting points% (default value is 0).%% See also: perform_fast_marching_3d.%% Copyright (c) 2007 Gabriel Peyreoptions.null = 0;end_points = getoptions(options, 'end_points', []);verbose = getoptions(options, 'verbose', 1);nb_iter_max = getoptions(options, 'nb_iter_max', Inf);values = getoptions(options, 'values', []);L = getoptions(options, 'constraint_map', []);H = getoptions(options, 'heuristic', []);dmax = getoptions(options, 'dmax', Inf);d = nb_dims(W);if (d==4 && size(W,3)==2 && size(W,4)==2) || (d==4 && size(W,4)==6) || (d==5 && size(W,4)==3 && size(W,5)==3) % anisotropic fast marching if d==4 && size(W,3)==2 && size(W,4)==2 % 2D vector field -> 3D field W1 = zeros(size(W,1), size(W,2), 3, 3); W1(:,:,1:2,1:2) = W; W1(:,:,3,3) = 1; W = reshape(W1, [size(W,1) size(W,2), 1 3 3]); % convert to correct size W = cat(4, W(:,:,:,1,1), W(:,:,:,1,2), W(:,:,:,1,3), W(:,:,:,2,2), W(:,:,:,2,3), W(:,:,:,3,3) ); end if d==5 % convert to correct size W = cat(4, W(:,:,:,1,1), W(:,:,:,1,2), W(:,:,:,1,3), W(:,:,:,2,2), W(:,:,:,2,3), W(:,:,:,3,3) ); end if size(start_points,1)==2 start_points(end+1,:) = 1; end if size(start_points,1)~=3 error('start_points should be of size (3,n)'); end % padd to avoid boundary problem W = cat(1, W(1,:,:,:), W, W(end,:,:,:)); W = cat(2, W(:,1,:,:), W, W(:,end,:,:)); W = cat(3, W(:,:,1,:), W, W(:,:,end,:)); % if isempty(L) L = ones(size(W,1), size(W,2), size(W,3)); % end if dmax==Inf dmax = 1e15; end % start_points = start_points-1; alpha = 0; [D,Q] = perform_front_propagation_anisotropic(W, L, alpha, start_points,dmax); % remove boundary problems D = D(2:end-1,2:end-1,2:end-1); Q = Q(2:end-1,2:end-1,2:end-1); S = []; D(D>1e20) = Inf; return;endif d~=2 && d~=3 error('Works only in 2D and 3D.');endif size(start_points,1)~=d error('start_points should be (d,k) dimensional with k=2 or 3.');endL(L==-Inf)=-1e9;L(L==Inf)=1e9;nb_iter_max = min(nb_iter_max, 1.2*max(size(W))^d);% use fast C-coded version if possibleif d==2 if exist('perform_front_propagation_2d')~=0 [D,S,Q] = perform_front_propagation_2d(W,start_points-1,end_points-1,nb_iter_max, H, L, values); %讲下vonoroi的分类原理, 假设初始sample点有k个,那么就把这k个sample点作为k个cell的中心,然后将剩下的点距离哪个sample点近就把归于哪个cell里 %跟那种用平面切出来的cell虽然过程不一样,但是原理是一样的.每一个sample点会拥有一个cell else [D,S] = perform_front_propagation_2d_slow(W,start_points,end_points,nb_iter_max, H); Q = []; endelseif d==3 [D,S,Q] = perform_front_propagation_3d(W,start_points-1,end_points-1,nb_iter_max, H, L, values);endQ = Q+1;% replace C 'Inf' value (1e9) by Matlab Inf value.D(D>1e8) = Inf;运行结果如下:
蓝色表示距离为0, 红色表示距离为1.
最后讲下该方法与medial axis的共同之处:
1. 最远点一定会落在中轴上面
证明: 最远点是指至少到两个点的距离相等,则此距离最远,那么它肯定满足距离相等这一条件,即它一定会落在中轴上面
2.它与power diagram的关系为:powder diagram插入球后, 相等于将把weight对应的球的区域设为0. weight值越小,排斥力越强, 越大,吸引力越强.如果把整个球的区域设为0.5,那么产生的中轴可能就是弧形,而不是直线.而且该弧开是比较靠近值大的球.