[37] | 1 | % * This code was used in the following articles:
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| 2 | % * [1] Learning 3-D Scene Structure from a Single Still Image,
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| 3 | % * Ashutosh Saxena, Min Sun, Andrew Y. Ng,
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| 4 | % * In ICCV workshop on 3D Representation for Recognition (3dRR-07), 2007.
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| 5 | % * (best paper)
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| 6 | % * [2] 3-D Reconstruction from Sparse Views using Monocular Vision,
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| 7 | % * Ashutosh Saxena, Min Sun, Andrew Y. Ng,
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| 8 | % * In ICCV workshop on Virtual Representations and Modeling
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| 9 | % * of Large-scale environments (VRML), 2007.
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| 10 | % * [3] 3-D Depth Reconstruction from a Single Still Image,
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| 11 | % * Ashutosh Saxena, Sung H. Chung, Andrew Y. Ng.
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| 12 | % * International Journal of Computer Vision (IJCV), Aug 2007.
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| 13 | % * [6] Learning Depth from Single Monocular Images,
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| 14 | % * Ashutosh Saxena, Sung H. Chung, Andrew Y. Ng.
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| 15 | % * In Neural Information Processing Systems (NIPS) 18, 2005.
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| 16 | % *
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| 17 | % * These articles are available at:
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| 18 | % * http://make3d.stanford.edu/publications
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| 19 | % *
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| 20 | % * We request that you cite the papers [1], [3] and [6] in any of
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| 21 | % * your reports that uses this code.
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| 22 | % * Further, if you use the code in image3dstiching/ (multiple image version),
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| 23 | % * then please cite [2].
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| 24 | % *
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| 25 | % * If you use the code in third_party/, then PLEASE CITE and follow the
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| 26 | % * LICENSE OF THE CORRESPONDING THIRD PARTY CODE.
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| 27 | % *
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| 28 | % * Finally, this code is for non-commercial use only. For further
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| 29 | % * information and to obtain a copy of the license, see
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| 30 | % *
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| 31 | % * http://make3d.stanford.edu/publications/code
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| 32 | % *
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| 33 | % * Also, the software distributed under the License is distributed on an
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| 34 | % * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
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| 35 | % * express or implied. See the License for the specific language governing
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| 36 | % * permissions and limitations under the License.
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| 37 | % *
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| 38 | % */
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| 39 | function [AveSupFea ] = AveSupFea(Sup, MedSup, SupFact, MultiScaleSupTable, FeaMax, pick) |
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| 40 | |
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| 41 | % this function generate the AveSupFea for multiScaleSup |
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| 42 | |
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| 43 | global H2; |
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| 44 | |
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| 45 | NuSup = setdiff(unique(Sup)',0); |
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| 46 | NuSupSize = size(NuSup,2); |
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| 47 | %FeaSup = zeros(NuSupSize,size([H2;H4],1)+1); |
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| 48 | %FeaSup = zeros(NuSupSize,size([H2],1)+1); |
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| 49 | FeaSup = zeros(NuSupSize,size([H2],1)*3+1); % add up vertical and horizontal features |
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| 50 | %size(FeaSup) |
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| 51 | l = 1; |
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| 52 | for j = NuSup |
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| 53 | mask = MedSup ==j; |
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| 54 | [y x] = find(mask); |
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| 55 | HRange = min(x):max(x); |
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| 56 | VRange = min(y):max(y); |
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| 57 | if sum(mask(:))==0 |
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| 58 | disp(' AveSupFea error'); |
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| 59 | end |
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| 60 | % FeaSup(l,:) = [j (mean(H2(:,mask)')./FeaMax(1,2:18)) (mean(H4(:,mask)')./FeaMax(1,19:35))]; |
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| 61 | if pick == 1 |
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| 62 | FeaSup(l,:) = [j (mean(H2(:,mask)')./sqrt(FeaMax(1,2:18))) ... |
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| 63 | (mean( reshape( H2(:,VRange,:), size(H2,1), [])')./sqrt(FeaMax(1,2:18))) ... |
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| 64 | (mean( reshape( H2(:,:,HRange), size(H2,1), [])')./sqrt(FeaMax(1,2:18)))]; % keeping the Sup index at the first column in FeaSup |
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| 65 | elseif pick == 2 |
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| 66 | FeaSup(l,:) = [j (mean(H2(:,mask)')./FeaMax(1,2:18)) ... |
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| 67 | (mean( reshape( H2(:,VRange,:), size(H2,1), [])')./(FeaMax(1,2:18))) ... |
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| 68 | (mean( reshape( H2(:,:,HRange), size(H2,1), [])')./(FeaMax(1,2:18)))]; % keeping the Sup index at the first column in FeaSup |
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| 69 | else |
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| 70 | FeaSup(l,:) = [j (mean(H2(:,mask)')./FeaMax(1,19:35)) ... |
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| 71 | (mean( reshape( H2(:,VRange,:), size(H2,1), [])')./(FeaMax(1,19:35))) ... |
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| 72 | (mean( reshape( H2(:,:,HRange), size(H2,1), [])')./(FeaMax(1,19:35)))]; % keeping the Sup index at the first column in FeaSup |
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| 73 | end |
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| 74 | l = l + 1; |
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| 75 | end |
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| 76 | |
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| 77 | disp('go to AveSupFea'); |
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| 78 | % calculate the MultiScale Fea of Sup |
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| 79 | FeaSize = (size(H2,1)); |
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| 80 | ScaleSize = size(MultiScaleSupTable,2)-1; |
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| 81 | AveSupFea = zeros(NuSupSize,FeaSize*ScaleSize); |
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| 82 | l = 1; |
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| 83 | for j = NuSup |
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| 84 | row = find(MultiScaleSupTable(:,1) == j); |
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| 85 | Target = MultiScaleSupTable( row, 2:end); |
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| 86 | MultiMask = (MultiScaleSupTable(:,2:end) == repmat(Target,[ NuSupSize 1])); |
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| 87 | ScaleMask = sum(MultiMask,1) > 1; % if Scale have more than one Sup in the save Scale |
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| 88 | MultiMask(row,ScaleMask) = false; % set the j to zeros |
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| 89 | Wei = repmat(SupFact(:,2),[1 ScaleSize]).*MultiMask; % get rid of the j sup |
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| 90 | Wei = Wei ./ repmat(sum(Wei,1),[NuSupSize 1]); % normalize the Wei to have sum to 1 |
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| 91 | AveSupMultScaleFea = sum(repmat(FeaSup(:,2:(FeaSize+1)),[1 1 ScaleSize]).*repmat(permute(Wei,[1 3 2]),[1 FeaSize 1]),1); |
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| 92 | AveSupFea(l,:) = AveSupMultScaleFea(:)'; |
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| 93 | l = l + 1; |
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| 94 | end |
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| 95 | AveSupFea = [FeaSup AveSupFea]; |
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| 96 | |
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| 97 | return; |
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| 98 | |
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| 99 | |
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