[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 [im, SupNeighborTable]=premergAllsuperpixel_efficient(im, Default) |
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| 40 | %(This is program conver a non_ordered non_connected superpixel image to |
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| 41 | % ordered superpixel image |
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| 42 | % input: |
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| 43 | % im = N by M matrix depends on the image size |
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| 44 | % Default - Default.SmallThre = the samllest Sup size |
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| 45 | |
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| 46 | % output: |
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| 47 | % im_order = N by M matrix which is ordered |
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| 48 | |
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| 49 | %%% |
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| 50 | % For each of the superpixel indicies, finds all of the |
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| 51 | % disconnected components with that label. if the component is |
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| 52 | % small (< 200 pixels) or isn't the biggest component with that |
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| 53 | % index, call analysesupinpatch with the outline of the component |
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| 54 | % to replace it's label with the one that is most common in the outline |
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| 55 | %%% |
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| 56 | |
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| 57 | if nargin <2 |
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| 58 | Default.SmallThre = 5; %smallest sup size |
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| 59 | end |
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| 60 | SupNeighborTableFlag = 1; |
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| 61 | |
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| 62 | [yn xn] = size(im); |
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| 63 | NuSup = unique(im(:))'; |
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| 64 | SE = strel('octagon',3); |
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| 65 | for i=NuSup |
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| 66 | |
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| 67 | % label connected component |
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| 68 | temp = zeros(size(im)); |
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| 69 | temp(im(:,:)==i)=1; |
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| 70 | [L,num] = bwlabel(temp,4); |
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| 71 | |
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| 72 | % find the main piece |
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| 73 | [maxL dum]= mode(L(L~=0)); |
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| 74 | % his = histc(L(:), 1:num); |
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| 75 | % [dum maxL ]= max(his); |
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| 76 | |
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| 77 | if dum > Default.SmallThre; |
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| 78 | SupMerg = setdiff(1:num,maxL); |
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| 79 | else |
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| 80 | SupMerg = 1:num; |
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| 81 | end |
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| 82 | |
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| 83 | for k = SupMerg |
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| 84 | mask = L==k; |
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| 85 | % then assign those pixels to mostlikely 3 by 3 neighborhood |
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| 86 | mask_dilate = imdilate(mask,SE); |
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| 87 | % mask_dilate = mask | [zeros(yn,1) mask(:,1:(end-1))] ... |
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| 88 | % | [mask(:,2:(end)) zeros(yn,1)] ... |
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| 89 | % | [zeros(1,xn) ;mask(1:(end-1),:)] ... |
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| 90 | % | [mask(2:(end),:); zeros(1, xn)] .... |
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| 91 | % | [[zeros(yn-1,1) mask(2:end,1:(end-1))]; zeros(1,xn)]... |
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| 92 | % | [[mask(2:end,2:(end)) zeros(yn-1,1) ]; zeros(1,xn)]... |
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| 93 | % | [zeros(1,xn); [zeros(yn-1,1) mask(1:(end-1),1:(end-1))]]... |
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| 94 | % | [zeros(1,xn); [mask(1:(end-1),2:(end)) zeros(yn-1,1)]]... |
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| 95 | % ; |
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| 96 | mask_dilate(mask) = 0; |
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| 97 | % im(mask) = analysesupinpatch(im(mask_dilate));%hard work |
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| 98 | im(mask) = mode(im(mask_dilate)); |
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| 99 | end |
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| 100 | end |
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| 101 | |
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| 102 | % merge the small superpixel with the surrrounding one if it's neighbor is only one |
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| 103 | MaxSupIndex = max(NuSup(:)); |
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| 104 | SupNeighborTable = sparse(MaxSupIndex,MaxSupIndex); |
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| 105 | |
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| 106 | if SupNeighborTableFlag |
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| 107 | for i = 1:((xn-1)*yn) |
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| 108 | |
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| 109 | % registed the neoghbor in right |
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| 110 | SupNeighborTable(im(i),im(i+yn)) = 1; |
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| 111 | SupNeighborTable(im(i+yn),im(i)) = 1; |
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| 112 | |
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| 113 | % registed the neoghbor in below |
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| 114 | if mod(i,yn) ~=0 |
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| 115 | SupNeighborTable(im(i),im(i+1)) = 1; |
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| 116 | SupNeighborTable(im(i+1),im(i)) = 1; |
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| 117 | end |
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| 118 | end |
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| 119 | |
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| 120 | % find out the single neighbor ones and merge them with neighbors |
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| 121 | SingleTar = sum( SupNeighborTable,1); |
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| 122 | for i = find(SingleTar == 1) |
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| 123 | mask = im == i; |
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| 124 | im(mask) = find(SupNeighborTable(:,i) == 1); |
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| 125 | SupNeighborTable(:,i) = 0; |
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| 126 | SupNeighborTable(i,:) = 0; |
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| 127 | end |
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| 128 | |
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| 129 | end |
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| 130 | return; |
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