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diff --git a/SD-VBS/benchmarks/sift/src/matlab/sift.m b/SD-VBS/benchmarks/sift/src/matlab/sift.m
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+% Input:
+%   I          the input image, with pixel values normalize to lie betwen [0,1].
+%
+% Output:
+%   features    a structure which contains the following fields:
+%       pos     an n*2 matrix containing the (x,y) coordinates of the keypoints
+%               stored in rows.
+%       scale   an n*3 matrix with rows describing the scale of each keypoint
+%               (i.e., first column specifies the octave, second column specifies the interval, and
+%               third column specifies sigma).
+%       orient  a n*1 vector containing the orientations of the keypoints [-pi,pi).
+%       desc    an n*128 matrix with rows containing the feature descriptors 
+%               corresponding to the keypoints.
+
+function [frames]=sift(I)
+
+[M,N,C] = size(I) ;
+
+% Lowe's choices
+S=3 ;
+omin=-1 ;
+O=floor(log2(min(M,N)))-omin-4 ; % Up to 16x16 images
+sigma0=1.6*2^(1/S) ;
+sigman=0.5 ;
+thresh = 0.04 / S / 2 ;
+r = 10 ;
+
+NBP = 4 ;
+NBO = 8 ;
+magnif = 3.0 ;
+
+% Parese input
+compute_descriptor = 0 ;
+discard_boundary_points = 1 ;
+verb = 0 ;
+
+smin = -1;
+smax = S+1;
+intervals = smax - smin + 1;
+
+
+% --------------------------------------------------------------------
+%                                                           Parameters
+% --------------------------------------------------------------------
+
+oframes = [] ;
+frames = [] ;
+descriptors = [] ;
+
+% --------------------------------------------------------------------
+%                                         SIFT Detector and Descriptor
+% --------------------------------------------------------------------
+
+% Compute scale spaces
+%if verb > 0, fprintf('SIFT: computing scale space...') ; end
+
+gss = gaussianss(I,sigman,O,S,omin,-1,S+1,sigma0) ;
+dogss = diffss(gss) ;
+frames = [];
+
+%% To maintain consistency with C code. Once C code is ready, this will be uncommented.
+for o=1:O                               %for o=1:O 
+        
+        % Local maxima of the DOG octave
+        % The 80% tricks discards early very weak points before refinement.
+         
+    idx = siftlocalmax(  dogss.octave{o}, 0.8*thresh  ) ;
+        idx = [idx , siftlocalmax( - dogss.octave{o}, 0.8*thresh)] ;  
+   
+        K=length(idx) ; 
+        [i,j,s] = ind2sub( size( dogss.octave{o} ), idx ) ;
+    
+    y=i-1 ;
+        x=j-1 ;
+        s=s-1+dogss.smin ;
+    oframes = [x(:)';y(:)';s(:)'] ;
+
+%    fWriteMatrix(oframes, '../data/sim');
+        
+        % Remove points too close to the boundary
+        if discard_boundary_points
+                oframes = filter_boundary_points(size(dogss.octave{o}), oframes ) ;
+        end
+                
+        % Refine the location, threshold strength and remove points on edges
+        oframes = siftrefinemx(...
+                oframes, ...
+                dogss.octave{o}, ...
+                dogss.smin, ...
+                thresh, ...
+                r) ;
+    
+   frames = [frames, oframes];     
+ 
+end
+end
+
+%% --------------------------------------------------------------------
+%%                                                              Helpers
+%% --------------------------------------------------------------------
+function oframes=filter_boundary_points(sz, oframes)
+
+sel=find(...
+        oframes(1,:) > 3 & ...
+        oframes(1,:) < sz(2)-3 & ...
+        oframes(2,:) > 3 & ...
+        oframes(2,:) < sz(1)-3 ) ;
+
+oframes=oframes(:,sel) ;
+end