[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 [SupFact, nList] = AnalyzeSup(Sup,maskSky) |
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| 40 | |
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| 41 | % this function analyze the Sup of the NuPatch in each Sup index |
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| 42 | NuSup = sort( setdiff( unique(Sup)',0)); |
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| 43 | edges =[ NuSup]; |
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| 44 | [yn xn] = size(Sup); |
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| 45 | |
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| 46 | % 1 ) NuPatch in each Sup |
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| 47 | SupFact = []; %NuSup' histc( Sup(:), edges) |
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| 48 | nList = []; |
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| 49 | for i = NuSup |
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| 50 | mask = Sup == i; |
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| 51 | SupFactTemp = []; |
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| 52 | |
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| 53 | % 2) Center Position of the Sup |
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| 54 | [y x] = find(mask); |
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| 55 | y50 = prctile(y,50); |
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| 56 | x50 = prctile(x,50); |
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| 57 | SupFactTemp = [SupFactTemp y50/yn x50/xn ]; |
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| 58 | |
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| 59 | % 3) x^2 y^2 position of superpixel |
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| 60 | SupFactTemp = [SupFactTemp SupFactTemp(:,(end-1):end).^2 ]; |
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| 61 | |
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| 62 | % 4) x y 10th & 90th |
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| 63 | y90 = prctile(y,90); |
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| 64 | x90 = prctile(x,90); |
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| 65 | y10 = prctile(y,10); |
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| 66 | x10 = prctile(x,10); |
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| 67 | SupFactTemp = [SupFactTemp y90/yn x90/xn y10/yn x10/xn ]; |
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| 68 | |
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| 69 | % 5) eccentricity |
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| 70 | x = x/xn; |
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| 71 | y = y/yn; |
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| 72 | C = cov([x y]); |
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| 73 | [v e] = eig(C); |
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| 74 | tt = diag(e); |
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| 75 | if size(tt,1)~=2 |
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| 76 | tt = [tt ;tt]; |
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| 77 | end |
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| 78 | [I C] = max(abs(tt)); |
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| 79 | ta = v(:,C).*sign(tt(C)); |
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| 80 | SupFactTemp = [SupFactTemp sqrt(abs(tt))' acos(ta(1))']; |
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| 81 | |
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| 82 | % 6) Neighbor count |
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| 83 | SE = strel('disk',3); |
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| 84 | mask_dilate = imdilate(mask,SE); |
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| 85 | mask_dilate_edge = mask_dilate; |
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| 86 | mask_dilate_edge(mask) = 0; |
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| 87 | mask_dilate_edge(maskSky) = 0; |
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| 88 | target =unique(Sup(mask_dilate_edge)); |
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| 89 | if any(target <= 0) || any(isnan(target)) |
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| 90 | disp('AnalyzeSup error'); |
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| 91 | end |
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| 92 | newNei = [i*ones(size(target,1),1) target]; |
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| 93 | newNei = sort(newNei,2); |
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| 94 | nList = [ nList; newNei]; |
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| 95 | SupFactTemp = [SupFactTemp size(target,1)]; |
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| 96 | |
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| 97 | SupFact = [SupFact; SupFactTemp]; |
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| 98 | end |
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| 99 | nList = unique(nList,'rows'); |
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| 100 | SupFact = [NuSup' histc( Sup(:), edges) SupFact]; |
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| 101 | return; |
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