source: proiecte/pmake3d/make3d_original/Make3dSingleImageStanford_version0.1/LearningCode/Unknow/LaserDetectOcc2Bp.m @ 37

Last change on this file since 37 was 37, checked in by (none), 14 years ago

Added original make3d

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1% *  This code was used in the following articles:
2% *  [1] Learning 3-D Scene Structure from a Single Still Image,
3% *      Ashutosh Saxena, Min Sun, Andrew Y. Ng,
4% *      In ICCV workshop on 3D Representation for Recognition (3dRR-07), 2007.
5% *      (best paper)
6% *  [2] 3-D Reconstruction from Sparse Views using Monocular Vision,
7% *      Ashutosh Saxena, Min Sun, Andrew Y. Ng,
8% *      In ICCV workshop on Virtual Representations and Modeling
9% *      of Large-scale environments (VRML), 2007.
10% *  [3] 3-D Depth Reconstruction from a Single Still Image,
11% *      Ashutosh Saxena, Sung H. Chung, Andrew Y. Ng.
12% *      International Journal of Computer Vision (IJCV), Aug 2007.
13% *  [6] Learning Depth from Single Monocular Images,
14% *      Ashutosh Saxena, Sung H. Chung, Andrew Y. Ng.
15% *      In Neural Information Processing Systems (NIPS) 18, 2005.
16% *
17% *  These articles are available at:
18% *  http://make3d.stanford.edu/publications
19% *
20% *  We request that you cite the papers [1], [3] and [6] in any of
21% *  your reports that uses this code.
22% *  Further, if you use the code in image3dstiching/ (multiple image version),
23% *  then please cite [2].
24% * 
25% *  If you use the code in third_party/, then PLEASE CITE and follow the
26% *  LICENSE OF THE CORRESPONDING THIRD PARTY CODE.
27% *
28% *  Finally, this code is for non-commercial use only.  For further
29% *  information and to obtain a copy of the license, see
30% *
31% *  http://make3d.stanford.edu/publications/code
32% *
33% *  Also, the software distributed under the License is distributed on an
34% * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
35% *  express or implied.   See the License for the specific language governing
36% *  permissions and limitations under the License.
37% *
38% */
39function [Bp, OccluMap, BoundaryLaserOccluHori, BoundaryLaserOccluVert] = ...
40          LaserDetectOcc2Bp( LaserDepth, BpWidthV, BpWidthH, BoundaryPVert, BoundaryPHori, Hash2Ind, dist)
41
42% This function assign 1 to the bounday labeled as occlusion with a probability distribution,
43% otherwise -1;
44if nargin <7
45   dist = 'uniform';
46end
47
48% inital parameters
49ThreVert = 0.5;
50ThreHori = 0.5;
51ThreFar = 15;
52SE = strel('rectangle',[BpWidthV BpWidthH]);
53
54
55DiffDepthVert = abs(conv2(LaserDepth,[1; -1],'valid'));
56DiffDepthHori = abs(conv2(LaserDepth,[1 -1],'valid'));
57FraDiffDepthVert = DiffDepthVert./ sqrt(LaserDepth(1:(end-1),:) .* LaserDepth(2:end,:) );
58%FraDiffDepthVert = DiffDepthVert./ min(LaserDepth(1:(end-1),:) , LaserDepth(2:end,:) );
59OccFraDiffDepthVert = FraDiffDepthVert > ThreVert;
60OccFraDiffDepthVert(LaserDepth(1:(end-1),:) > ThreFar & LaserDepth(2:end,:) > ThreFar) = 0;
61FraDiffDepthHori = DiffDepthHori./ sqrt( LaserDepth(:,1:(end-1)) .* LaserDepth(:,2:end) );
62%FraDiffDepthHori = DiffDepthHori./ min( LaserDepth(:,1:(end-1)) , LaserDepth(:,2:end) );
63OccFraDiffDepthHori = FraDiffDepthHori > ThreHori;
64OccFraDiffDepthHori(LaserDepth(:,1:(end-1)) > ThreFar & LaserDepth(:,2:end) > ThreFar) = 0;
65
66% form the OccluMap and BoundaryLaserOccluHori, BoundaryLaserOccluVert
67BoundaryLaserOccluHori = imdilate(OccFraDiffDepthVert, SE).*(BoundaryPHori ~=0 );
68BoundaryLaserOccluVert = imdilate(OccFraDiffDepthHori, SE).*(BoundaryPVert ~=0 );
69OccluMap = zeros(size(LaserDepth));
70if ~strcmp(dist,'uniform')
71    %assume norm dis
72     
73end
74   OccluMap(1:(end-1),:) = BoundaryLaserOccluHori;
75   OccluMap(2:(end),:) = BoundaryLaserOccluHori;
76   OccluMap(:,1:(end-1)) = BoundaryLaserOccluVert;
77   OccluMap(:,2:(end)) = BoundaryLaserOccluVert;
78
79% specifiy the label in Bp
80Boundary = setdiff(unique([BoundaryPVert( (BoundaryLaserOccluVert ~= 0)); BoundaryPHori( (BoundaryLaserOccluHori ~= 0))]), 0);
81Bp = -ones( sum(Hash2Ind ~=0),1);
82Bp(Hash2Ind(Boundary)) = 1;
83% Bp = -ones( sum(Hash2Ind ~=0),1);
84% for i = Boundary'
85%    maskH = BoundaryPVert == i;
86%    maskV = BoundaryPHori == i;
87% %    Bp(Hash2Ind(i)) = mean( [BoundaryLaserOccluVert(maskV); BoundaryLaserOccluHori(maskH) ]);
88%    Bp(Hash2Ind(i)) = any( [BoundaryLaserOccluVert(maskV); BoundaryLaserOccluHori(maskH) ] == 1);
89% end
90
91return;
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