[37] | 1 | % * This code was used in the following articles:
|
---|
| 2 | % * [1] Learning 3-D Scene Structure from a Single Still Image,
|
---|
| 3 | % * Ashutosh Saxena, Min Sun, Andrew Y. Ng,
|
---|
| 4 | % * In ICCV workshop on 3D Representation for Recognition (3dRR-07), 2007.
|
---|
| 5 | % * (best paper)
|
---|
| 6 | % * [2] 3-D Reconstruction from Sparse Views using Monocular Vision,
|
---|
| 7 | % * Ashutosh Saxena, Min Sun, Andrew Y. Ng,
|
---|
| 8 | % * In ICCV workshop on Virtual Representations and Modeling
|
---|
| 9 | % * of Large-scale environments (VRML), 2007.
|
---|
| 10 | % * [3] 3-D Depth Reconstruction from a Single Still Image,
|
---|
| 11 | % * Ashutosh Saxena, Sung H. Chung, Andrew Y. Ng.
|
---|
| 12 | % * International Journal of Computer Vision (IJCV), Aug 2007.
|
---|
| 13 | % * [6] Learning Depth from Single Monocular Images,
|
---|
| 14 | % * Ashutosh Saxena, Sung H. Chung, Andrew Y. Ng.
|
---|
| 15 | % * In Neural Information Processing Systems (NIPS) 18, 2005.
|
---|
| 16 | % *
|
---|
| 17 | % * These articles are available at:
|
---|
| 18 | % * http://make3d.stanford.edu/publications
|
---|
| 19 | % *
|
---|
| 20 | % * We request that you cite the papers [1], [3] and [6] in any of
|
---|
| 21 | % * your reports that uses this code.
|
---|
| 22 | % * Further, if you use the code in image3dstiching/ (multiple image version),
|
---|
| 23 | % * then please cite [2].
|
---|
| 24 | % *
|
---|
| 25 | % * If you use the code in third_party/, then PLEASE CITE and follow the
|
---|
| 26 | % * LICENSE OF THE CORRESPONDING THIRD PARTY CODE.
|
---|
| 27 | % *
|
---|
| 28 | % * Finally, this code is for non-commercial use only. For further
|
---|
| 29 | % * information and to obtain a copy of the license, see
|
---|
| 30 | % *
|
---|
| 31 | % * http://make3d.stanford.edu/publications/code
|
---|
| 32 | % *
|
---|
| 33 | % * Also, the software distributed under the License is distributed on an
|
---|
| 34 | % * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
|
---|
| 35 | % * express or implied. See the License for the specific language governing
|
---|
| 36 | % * permissions and limitations under the License.
|
---|
| 37 | % *
|
---|
| 38 | % */
|
---|
| 39 | function [ track]=TrackBuilding(Matches) |
---|
| 40 | |
---|
| 41 | % This function given the Matches between all pairs of images |
---|
| 42 | % generate the track without any inconsistent (delete the track with any inconsistency): |
---|
| 43 | % in that one image observes multiple features in the track |
---|
| 44 | |
---|
| 45 | % add image in order from img1, img2, to imgxxx |
---|
| 46 | % When add new image look for matches between added images |
---|
| 47 | % and maintaining the matches |
---|
| 48 | |
---|
| 49 | NumImages = size(Matches,1); |
---|
| 50 | track = sparse(0,NumImages); |
---|
| 51 | InConsistMatchesBin = cell(1,NumImages); |
---|
| 52 | NumTrack = size( track,1); |
---|
| 53 | for i = 1:NumImages |
---|
| 54 | if i ==1 % can build track with only one image |
---|
| 55 | continue; |
---|
| 56 | end |
---|
| 57 | for j = 1:i-1 |
---|
| 58 | % i |
---|
| 59 | % j |
---|
| 60 | % Skip if Matches(j,i).Index is empty |
---|
| 61 | if isempty(Matches(j,i).Index) |
---|
| 62 | continue; |
---|
| 63 | end |
---|
| 64 | |
---|
| 65 | % Clean the Matches using InConsistMatchesBin |
---|
| 66 | NumInconsistMatchesOfJ = size(InConsistMatchesBin{j},1); |
---|
| 67 | if NumInconsistMatchesOfJ ~=0 |
---|
| 68 | NumMatces = size(Matches(j,i).Index, 2); |
---|
| 69 | DeleteMark = sum(repmat( Matches(j,i).Index, NumInconsistMatchesOfJ, 1) == repmat(InConsistMatchesBin{j}, 1, NumMatces), 1) ~= 0; |
---|
| 70 | Matches(j,i).Index(DeleteMark) = []; |
---|
| 71 | Matches(i,j).Index(DeleteMark) = []; |
---|
| 72 | end |
---|
| 73 | |
---|
| 74 | NumMatces = size(Matches(j,i).Index, 2); |
---|
| 75 | % editing existing track |
---|
| 76 | if NumTrack ~=0 |
---|
| 77 | Ptr = (repmat(track(:,j), 1, NumMatces) == ... |
---|
| 78 | repmat( Matches(j,i).Index, NumTrack, 1)); |
---|
| 79 | Target = repmat( Matches(i,j).Index, NumTrack, 1); |
---|
| 80 | NewTarget = sparse(NumTrack, 1); |
---|
| 81 | NewTarget(sum(Ptr,2)~=0) = sum( Target(Ptr), 2); |
---|
| 82 | Target = NewTarget; |
---|
| 83 | Matches_for_new_track_I = setdiff(Matches(i,j).Index, Target'); |
---|
| 84 | % check consistency |
---|
| 85 | InConsistMark = (Target ~= track(:,i)) & (track(:,i) ~= 0); |
---|
| 86 | track(:,i) = Target; |
---|
| 87 | % maintina the InConsistMatchesBin |
---|
| 88 | if ~all(InConsistMark==0) |
---|
| 89 | InConsistMatchesBin{j} = union(InConsistMatchesBin{j}, track(InConsistMark,j)); |
---|
| 90 | InConsistMatchesBin{i} = track(InConsistMark,i); |
---|
| 91 | track(InConsistMark,:) = []; |
---|
| 92 | end |
---|
| 93 | else |
---|
| 94 | Matches_for_new_track_I = Matches(i,j).Index; |
---|
| 95 | end |
---|
| 96 | |
---|
| 97 | % add new track |
---|
| 98 | NumNewTrack = length(Matches_for_new_track_I); |
---|
| 99 | Matches_for_new_track_mark_I = sum( repmat( Matches(i,j).Index, NumNewTrack, 1) ==... |
---|
| 100 | repmat( Matches_for_new_track_I', 1, NumMatces), 1) ~=0; |
---|
| 101 | NewTrack = sparse(NumNewTrack, NumImages); |
---|
| 102 | NewTrack(:,j) = Matches(j,i).Index(Matches_for_new_track_mark_I)'; |
---|
| 103 | NewTrack(:,i) = Matches(i,j).Index(Matches_for_new_track_mark_I)'; |
---|
| 104 | track = [track; NewTrack]; |
---|
| 105 | |
---|
| 106 | % maintain Num of track |
---|
| 107 | NumTrack = size( track,1); |
---|
| 108 | end |
---|
| 109 | end |
---|