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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