[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 [FeatureSup]= f_sup_oldModify(Default,sup) |
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| 40 | % This fuction calculate the features of each superpixel (total 14) |
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| 41 | |
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| 42 | %%% |
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| 43 | % loads a new constant from generalData/SFeaMax.mat |
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| 44 | % |
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| 45 | % then for each superpixel index, calculate 13 features (each is |
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| 46 | % divided by a number in SFeaMax) |
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| 47 | % |
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| 48 | % 1) % of image spanned by this superpixel |
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| 49 | % 2) x and y position of the center of mass of the superpixel [0-1] |
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| 50 | % 3) x and y squared |
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| 51 | % 4) x and y positions of 10% and 90% of mass [0-1] |
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| 52 | % 5) # of unique superpixels that border this one |
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| 53 | % 6) angle of the principal direction of the shape of this |
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| 54 | % superpixel (max eigenvector) and sqrt(max eigenvalue) |
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| 55 | %%%% |
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| 56 | |
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| 57 | % normalize the Superpixel feature according to the previous result |
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| 58 | load([Default.SFeaPara '/SFeaMax.mat']); |
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| 59 | NormalizeFlag = 0; |
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| 60 | % SFeaMax = 10.^floor(log10(SFeaMax)); |
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| 61 | if NormalizeFlag == 1 |
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| 62 | SFeaMax = 10.^floor(log10(SFeaMax)); |
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| 63 | else |
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| 64 | SFeaMax(:) = 1; |
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| 65 | end |
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| 66 | |
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| 67 | FeatureSup = []; |
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| 68 | [yn xn] = size(sup); |
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| 69 | NuSup = max(max(sup)); |
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| 70 | for i=1:NuSup |
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| 71 | % tic; |
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| 72 | l = 1; |
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| 73 | FeaturePicsSup = []; |
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| 74 | mask = sup==i; |
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| 75 | % calculating feature |
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| 76 | % 1) size of superpixel normalized to the total number of subsuperpixel |
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| 77 | NuSub = sum(sum(mask)); |
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| 78 | FeaturePicsSup = [FeaturePicsSup; NuSub/(xn*yn)/SFeaMax(1,l)]; |
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| 79 | l=l+1; |
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| 80 | % 2) x y position of superpixel |
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| 81 | [y x] = find(mask); |
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| 82 | y50 = prctile(y,50)/yn; |
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| 83 | x50 = prctile(x,50)/xn; |
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| 84 | FeaturePicsSup = [FeaturePicsSup; x50/SFeaMax(1,l); y50/SFeaMax(1,l+1)]; |
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| 85 | l = l+2; |
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| 86 | % 3) x^2 y^2 position of superpixel |
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| 87 | FeaturePicsSup = [FeaturePicsSup; (x50)^2/SFeaMax(1,l); (y50)^2/SFeaMax(1,l+1)]; |
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| 88 | l = l+1; |
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| 89 | % 4) x y 10th & 90th |
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| 90 | y90 = prctile(y,90)/yn; |
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| 91 | x90 = prctile(x,90)/xn; |
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| 92 | y10 = prctile(y,10)/yn; |
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| 93 | x10 = prctile(x,10)/xn; |
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| 94 | FeaturePicsSup = [FeaturePicsSup; x10/SFeaMax(1,l); y10/SFeaMax(1,l+1); x90/SFeaMax(1,l+2); y90/SFeaMax(1,l+3)]; |
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| 95 | l = l+4; |
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| 96 | % 5) number of connected superpixel |
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| 97 | % if i==56 |
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| 98 | % disp('i=56'); |
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| 99 | % end |
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| 100 | SE = strel('diamond',3); |
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| 101 | mask_dilate = imdilate(mask,SE); |
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| 102 | mask_dilate_edge = mask_dilate; |
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| 103 | mask_dilate_edge(mask) = 0; |
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| 104 | [list_sup] = unique(sup(mask_dilate_edge)); %hard work |
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| 105 | FeaturePicsSup = [FeaturePicsSup; size(list_sup,1)/SFeaMax(1,l)]; |
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| 106 | l=l+1; |
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| 107 | % 6) eccentricity |
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| 108 | [y x] = find(mask); |
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| 109 | x = x/xn; |
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| 110 | y = y/yn; |
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| 111 | C = cov([x y]); |
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| 112 | [v e] = eig(C); |
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| 113 | tt = diag(e); |
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| 114 | if size(tt,1)~=2 |
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| 115 | tt = [tt ;tt]; |
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| 116 | end |
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| 117 | [I C] = max(abs(tt)); |
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| 118 | ta = v(:,C).*sign(tt(C)); |
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| 119 | |
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| 120 | % abs(tt): the standard deviation of the prime axis |
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| 121 | % v(:,C).*sign(tt(C)) : the direction of the prime axis |
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| 122 | FeaturePicsSup = [FeaturePicsSup; sqrt(abs(tt))/SFeaMax(1,l); acos(ta(1))/SFeaMax(1,l+1)]; |
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| 123 | if size(FeaturePicsSup,1) ~=13 |
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| 124 | disp('error'); |
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| 125 | end |
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| 126 | %l=l+1; |
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| 127 | %MIN: Convert the eigenvector to a positive angle between 0 to 360 |
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| 128 | %FeaturePicsSup = [FeaturePicsSup; sqrt(abs(tt)); abs(v(:,C))]; |
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| 129 | FeatureSup = [FeatureSup FeaturePicsSup]; |
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| 130 | % toc; |
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| 131 | end |
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