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 FeaSupNeighList=findBoundaryFeaturesMore(Sup_low,Sup_medi,nList,seglist, FeaMax, slack) |
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40 | %tic |
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41 | %load ../debugFeat.mat |
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42 | global H2; |
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43 | FeaSize = size(H2,1) |
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44 | |
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45 | NuSup = setdiff(unique(Sup_low)',0); |
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46 | NuSup = sort(NuSup); |
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47 | |
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48 | [currMask1, xVec1, yVec1, currMask2, xVec2, yVec2]=creatingMasks(Sup_low,Sup_medi); |
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49 | % load /afs/cs/group/reconstruction3d/scratch/maskSup1.mat |
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50 | % load /afs/cs/group/reconstruction3d/scratch/maskSup2.mat |
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51 | |
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52 | mask = zeros(size(Sup_medi,1),size(Sup_medi,2)); |
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53 | bMask = mask; |
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54 | bCurrNeighMask = mask; |
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55 | |
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56 | %nRows = size(mask,1); |
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57 | %nCols = size(mask,2); |
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58 | %newMask = zeros(nRows,nCols,2); |
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59 | %colCount = 1:nCols; |
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60 | %rowCount = (1:nRows)'; |
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61 | %newMask(:,:,1) = repmat(colCount,nRows,1); |
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62 | %newMask(:,:,2) = repmat(rowCount,1,nCols); |
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63 | |
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64 | currNeighMask=zeros(size(Sup_medi,1),size(Sup_medi,2)); |
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65 | xV=zeros(1,size(mask,2)); |
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66 | yV=zeros(1,size(mask,1)); |
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67 | FeaSupNeighList = zeros(size(NuSup,2),FeaSize); |
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68 | |
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69 | countSup=1; |
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70 | for i = NuSup |
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71 | nListCurr=find(nList(:,1)==i); |
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72 | posInNuSup=find(NuSup==i); |
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73 | if (posInNuSup<=500) |
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74 | mask = full(currMask1{posInNuSup}); |
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75 | xV=xVec1{posInNuSup}; |
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76 | yV=yVec1{posInNuSup}; |
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77 | else |
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78 | mask = full(currMask2{posInNuSup-500}); |
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79 | xV=xVec2{posInNuSup-500}; |
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80 | yV=yVec2{posInNuSup-500}; |
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81 | end |
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82 | %% find midpoint of this superpixel |
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83 | %% Notice that the sum command sums the columns of a matrix |
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84 | x(1)=findSupMid(xV); |
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85 | y(1)=findSupMid(yV); |
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86 | |
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87 | |
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88 | for j=1:length(nListCurr) |
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89 | neighNum=nList(nListCurr(j),2); |
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90 | posInNuSup=find(NuSup==neighNum); |
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91 | if (posInNuSup<=500) |
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92 | currNeighMask = full(currMask1{posInNuSup}); |
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93 | xV=xVec1{posInNuSup}; |
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94 | yV=yVec1{posInNuSup}; |
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95 | else |
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96 | currNeighMask = full(currMask2{posInNuSup-500}); |
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97 | xV=xVec2{posInNuSup-500}; |
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98 | yV=yVec2{posInNuSup-500}; |
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99 | end |
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100 | |
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101 | %% Now we find the mid-point of the neighbor superpixel |
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102 | |
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103 | x(2)=findSupMid(xV); |
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104 | y(2)=findSupMid(yV); |
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105 | |
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106 | %iPos=find(FeaSupList(1,:)==i); |
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107 | %jPos=find(FeaSupList(1,:)==neighNum); |
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108 | |
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109 | %Xi = FeaSupList(2:end,iPos); |
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110 | %Xj = FeaSupList(2:end,jPos); |
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111 | |
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112 | %FeaSupNeighList(countSup,3:(3+(length(Xi))-1)) =abs(Xi-Xj)'; |
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113 | |
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114 | % %% Now we calculate the features with just average over the |
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115 | % %% boundary |
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116 | % [xCen,yCen]=findIntersectionPoint(mask,currNeighMask,x(1:2),y(1:2),newMask); |
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117 | xCen = mean(x); |
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118 | yCen = mean(y); |
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119 | R = [sqrt((xCen-x(1))^2+(yCen-y(1))^2);sqrt((x(2)-xCen)^2+(y(2)-yCen)^2)]; |
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120 | R=R-slack*R; |
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121 | |
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122 | bMask = findCloserPointsSquare(mask,xCen,yCen,R(1)); |
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123 | bCurrNeighMask = findCloserPointsSquare(currNeighMask,xCen,yCen,R(2)); |
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124 | |
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125 | % bMask = findCloserPointsSquare(mask,xCen,yCen,R(1)); |
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126 | % bCurrNeighMask = findCloserPointsSquare(currNeighMask,xCen,yCen,R(2)); |
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127 | % |
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128 | if ((sum(sum(bMask))==0)||(sum(sum(bCurrNeighMask))==0)) |
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129 | bXi = zeros(1,FeaSize); |
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130 | bXj = zeros(1,FeaSize); |
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131 | else |
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132 | bXi = (mean(H2(:,logical(bMask))')./FeaMax(1,2:18)); %% Min change this to 18 |
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133 | bXj = (mean(H2(:,logical(bCurrNeighMask))')./FeaMax(1,2:18)); %% Min change this to 18 |
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134 | end |
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135 | |
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136 | FeaSupNeighList(countSup,1:FeaSize) =abs(bXi-bXj); |
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137 | %% Now we check if any of the lines intersects the current |
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138 | %% super-pixel and its current neighbor |
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139 | FeaSupNeighList(countSup,FeaSize+1)=checkIfALineCrosses(seglist,x,y); |
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140 | % for k=1:size(seglist,1) |
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141 | % x(3)=seglist(k,1); |
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142 | % y(3)=seglist(k,2); |
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143 | % x(4)=seglist(k,3); |
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144 | % y(4)=seglist(k,4); |
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145 | % if(lineSegIntersect(x,y)) |
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146 | % bndry_features(countSup,3)=1; |
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147 | % break; |
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148 | % end |
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149 | % end |
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150 | |
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151 | % FeaSupNeighList(countSup,(3+length(Xi)+length(bXi))) = bndry_features(countSup,3); |
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152 | % FeaSupNeighList(countSup,(3+length(Xi))) = bndry_features(countSup,3); |
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153 | countSup=countSup+1; |
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154 | end |
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155 | end |
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156 | |
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157 | size(FeaSupNeighList) |
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