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 [MedSup, Sup, Default SupNeighborTableFlag]=gen_Sup_efficient_mod(Default, img) |
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40 | function Sup=gen_Sup_efficient_mod(img) |
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41 | % this function generate superpixel using default parameter |
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42 | % but can also change to manually input parameter |
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43 | |
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44 | %%% Jeff's Comments (Min modified) |
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45 | % Send that image to the CMU |
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46 | % segmentation program with params 0.8*[sigm, k, min]. If |
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47 | % SelectSegmenationPara is true, then display the results and ask |
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48 | % the user to enter new sigm, k, and min; repeat until user is happy. |
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49 | % |
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50 | % Output the MedSup(only the smallest scale) and Sup(three scale) |
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51 | %%%% |
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52 | |
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53 | % default parameter |
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54 | % if nargin < 3 |
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55 | % SelectSegmentationPara = 0; % if SelectSegmentationPara == 1, enable the parameter interation with user. |
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56 | % end |
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57 | % DisplayFlag = Default.Flag.DisplayFlag; % set to display or not |
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58 | |
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59 | % adding paths |
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60 | addpath(genpath('../Features')); |
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61 | |
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62 | %scale =[0.8 1.6 5]; % use different scale to generate small(0.8) middle(1.6) 5(large) scale of superpixel |
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63 | scale = 0.8; |
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64 | |
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65 | % setup the default for Superpixel segmentation |
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66 | Default.SegVertYSize = 900; |
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67 | Default.SegHoriXSize = 1200; |
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68 | Default.sigm = .5; %parameter to smoothe the image |
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69 | Default.k = 50; %constant for threshold function |
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70 | Default.minp = 80; %minimum component size |
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71 | Default.OutPut = './outfolder/test.ppm'; |
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72 | Default.PpmOption = 1; %pick random color for each component |
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73 | Default.VertYNuDepth = 500; |
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74 | Default.HoriXNuDepth = 600; |
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75 | Default.SmallThre = 5; |
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76 | |
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77 | %start segmenting |
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78 | [VertYSizeHiREs HoriXSizeHiREs dummy]= size(img);% find the dimension size of the Hi Resolution image |
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79 | clear dummy; |
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80 | |
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81 | % using a fixed range of median size image [SegVertYSize SegHoriXSize ] |
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82 | % to generate superpixel to reduce computation |
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83 | if VertYSizeHiREs*HoriXSizeHiREs > Default.SegVertYSize*Default.SegHoriXSize |
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84 | |
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85 | % Downsample high resolution image to a fixed median size image |
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86 | %**** +4 because segmentImgOpt gives constant additinal rows and column |
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87 | %**** so add 4 rows and columns a prior then delete then at line 55 |
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88 | img = imresize(img,[Default.SegVertYSize+4 Default.SegHoriXSize+4 ],'nearest'); |
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89 | else |
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90 | Default.SegVertYSize = VertYSizeHiREs-4; |
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91 | Default.SegHoriXSize = HoriXSizeHiREs-4; |
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92 | end |
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93 | |
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94 | % generate superpixel of each image |
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95 | %for j = 1:length(scale)% number of scale of superpixel |
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96 | |
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97 | % choose superpixel of the images |
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98 | % default segmentation parameter |
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99 | % ok = 0; % ok ==1 means accept the segmentation |
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100 | % while 1 |
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101 | |
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102 | % call the efficient segment function writen in C++ from MIT |
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103 | % Output the high resolution image ( + 1 since the smallest index can be zero) |
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104 | % if j ==1 |
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105 | % a = segmentImgOpt( Default.sigm*scale(j), Default.k*scale(j), Default.minp*scale(j), img,... |
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106 | % [ Default.OutPutFolder Default.filename{1} '.ppm'],Default.PpmOption) + 1; |
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107 | a = segmentImgOpt( Default.sigm*scale, Default.k*scale, Default.minp*scale, img,... |
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108 | Default.OutPut, Default.PpmOption) + 1; |
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109 | |
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110 | %ppmoption controls the output colormap, 1--generate random |
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111 | %colormap, 0--keep the current colormap |
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112 | % else |
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113 | % a = segmentImgOpt( Default.sigm*scale(j), Default.k*scale(j), Default.minp*scale(j), img,... |
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114 | % [ Default.OutPutFolder Default.filename{1} '.ppm'], 0) + 1; |
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115 | % end |
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116 | a = a(3:(end-2),3:(end-2)); %*** clean the edge superpixel index errors *** |
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117 | |
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118 | % Arrange the superpixel index in order |
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119 | % if j == 1 % For the smallest Scale |
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120 | |
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121 | ma = max(a(:)); |
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122 | Unique_a = unique(a); |
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123 | SparseIndex = sparse(ma,1); |
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124 | SparseIndex(Unique_a) = 1:size(Unique_a); |
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125 | MedSup = full(SparseIndex(a)); |
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126 | |
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127 | % %Downsample to size as prediected depth map |
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128 | Sup = imresize(MedSup,[Default.VertYNuDepth Default.HoriXNuDepth],'nearest'); |
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129 | % Sup{j} = imresize(MedSup,[Default.VertYNuDepth Default.HoriXNuDepth],'nearest'); |
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130 | % % clean superpixel section ==================================================================== |
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131 | % % merage all small and disconneted points in 1st scale segmentation |
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132 | [Sup SupNeighborTableFlag] = premergAllsuperpixel_efficient(Sup, Default); |
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133 | % [Sup{j} SupNeighborTableFlag] = premergAllsuperpixel_efficient(Sup{j}, Default); |
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134 | % % ============================================================== |
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135 | % =============================== |
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136 | |
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137 | %else % o/w don't need the MedSup |
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138 | |
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139 | %Downsample to size size as prediected depth map |
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140 | % a = imresize(a,[Default.VertYNuDepth Default.HoriXNuDepth],'nearest'); |
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141 | % ma = max(a(:)); |
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142 | % Unique_a = unique(a); |
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143 | % SparseIndex = sparse(ma,1); |
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144 | % SparseIndex(Unique_a) = 1:size(Unique_a); |
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145 | % Sup{j} = full(SparseIndex(a)); |
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146 | % end |
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147 | clear a SparseIndex Unique_a ma; |
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148 | |
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149 | |
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150 | % show superpixel |
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151 | % if DisplayFlag |
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152 | % figure(1); |
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153 | % imagesc(Sup{j}); |
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154 | % newmap = rand(max(max(Sup{j})),3); |
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155 | % colormap(newmap); |
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156 | % end |
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157 | |
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158 | % check if need to select segmentation parameter |
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159 | % if SelectSegmentationPara==1; |
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160 | % ok = input('Is the segmentation of image OK');% input new segmentation parameter |
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161 | % else |
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162 | % ok =1 ;% accept default segmentation parameter |
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163 | % end |
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164 | % |
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165 | % if ok==1; |
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166 | % break; |
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167 | % end |
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168 | |
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169 | % Get the user selected parameter |
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170 | % sigm = input('type sigm of segmentation'); |
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171 | % k = input('type k of segmentation'); |
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172 | % minp = input('type min of segmentation'); |
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173 | |
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174 | % end % end of while 1 |
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175 | |
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176 | %end % end of for j=1:3 |
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177 | |
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178 | %return; |
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