% * This code was used in the following articles: % * [1] Learning 3-D Scene Structure from a Single Still Image, % * Ashutosh Saxena, Min Sun, Andrew Y. Ng, % * In ICCV workshop on 3D Representation for Recognition (3dRR-07), 2007. % * (best paper) % * [2] 3-D Reconstruction from Sparse Views using Monocular Vision, % * Ashutosh Saxena, Min Sun, Andrew Y. Ng, % * In ICCV workshop on Virtual Representations and Modeling % * of Large-scale environments (VRML), 2007. % * [3] 3-D Depth Reconstruction from a Single Still Image, % * Ashutosh Saxena, Sung H. Chung, Andrew Y. Ng. % * International Journal of Computer Vision (IJCV), Aug 2007. % * [6] Learning Depth from Single Monocular Images, % * Ashutosh Saxena, Sung H. Chung, Andrew Y. Ng. % * In Neural Information Processing Systems (NIPS) 18, 2005. % * % * These articles are available at: % * http://make3d.stanford.edu/publications % * % * We request that you cite the papers [1], [3] and [6] in any of % * your reports that uses this code. % * Further, if you use the code in image3dstiching/ (multiple image version), % * then please cite [2]. % * % * If you use the code in third_party/, then PLEASE CITE and follow the % * LICENSE OF THE CORRESPONDING THIRD PARTY CODE. % * % * Finally, this code is for non-commercial use only. For further % * information and to obtain a copy of the license, see % * % * http://make3d.stanford.edu/publications/code % * % * Also, the software distributed under the License is distributed on an % * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either % * express or implied. See the License for the specific language governing % * permissions and limitations under the License. % * % */ function [ track]=TrackBuilding(Matches) % This function given the Matches between all pairs of images % generate the track without any inconsistent (delete the track with any inconsistency): % in that one image observes multiple features in the track % add image in order from img1, img2, to imgxxx % When add new image look for matches between added images % and maintaining the matches NumImages = size(Matches,1); track = sparse(0,NumImages); InConsistMatchesBin = cell(1,NumImages); NumTrack = size( track,1); for i = 1:NumImages if i ==1 % can build track with only one image continue; end for j = 1:i-1 % i % j % Skip if Matches(j,i).Index is empty if isempty(Matches(j,i).Index) continue; end % Clean the Matches using InConsistMatchesBin NumInconsistMatchesOfJ = size(InConsistMatchesBin{j},1); if NumInconsistMatchesOfJ ~=0 NumMatces = size(Matches(j,i).Index, 2); DeleteMark = sum(repmat( Matches(j,i).Index, NumInconsistMatchesOfJ, 1) == repmat(InConsistMatchesBin{j}, 1, NumMatces), 1) ~= 0; Matches(j,i).Index(DeleteMark) = []; Matches(i,j).Index(DeleteMark) = []; end NumMatces = size(Matches(j,i).Index, 2); % editing existing track if NumTrack ~=0 Ptr = (repmat(track(:,j), 1, NumMatces) == ... repmat( Matches(j,i).Index, NumTrack, 1)); Target = repmat( Matches(i,j).Index, NumTrack, 1); NewTarget = sparse(NumTrack, 1); NewTarget(sum(Ptr,2)~=0) = sum( Target(Ptr), 2); Target = NewTarget; Matches_for_new_track_I = setdiff(Matches(i,j).Index, Target'); % check consistency InConsistMark = (Target ~= track(:,i)) & (track(:,i) ~= 0); track(:,i) = Target; % maintina the InConsistMatchesBin if ~all(InConsistMark==0) InConsistMatchesBin{j} = union(InConsistMatchesBin{j}, track(InConsistMark,j)); InConsistMatchesBin{i} = track(InConsistMark,i); track(InConsistMark,:) = []; end else Matches_for_new_track_I = Matches(i,j).Index; end % add new track NumNewTrack = length(Matches_for_new_track_I); Matches_for_new_track_mark_I = sum( repmat( Matches(i,j).Index, NumNewTrack, 1) ==... repmat( Matches_for_new_track_I', 1, NumMatces), 1) ~=0; NewTrack = sparse(NumNewTrack, NumImages); NewTrack(:,j) = Matches(j,i).Index(Matches_for_new_track_mark_I)'; NewTrack(:,i) = Matches(i,j).Index(Matches_for_new_track_mark_I)'; track = [track; NewTrack]; % maintain Num of track NumTrack = size( track,1); end end