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diff --git a/SD-VBS/benchmarks/sift/src/matlab/siftlocalmax.m b/SD-VBS/benchmarks/sift/src/matlab/siftlocalmax.m
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-% SIFTLOCALMAX  Find local maximizers
-%   SEL=SIFTLOCALMAX(F) returns the indexes of the local maximizers of
-%   the Q-dimensional array F.
-%
-%   A local maximizer is an element whose value is greater than the
-%   value of all its neighbors.  The neighbors of an element i1...iQ
-%   are the subscripts j1...jQ such that iq-1 <= jq <= iq (excluding
-%   i1...iQ itself).  For example, if Q=1 the neighbors of an element
-%   are its predecessor and successor in the linear order; if Q=2, its
-%   neighbors are the elements immediately to its north, south, west,
-%   est, north-west, north-est, south-west and south-est
-%   (8-neighborhood).
-%
-%   Points on the boundary of F are ignored (and never selected as
-%   local maximizers).
-%
-%   SEL=SIFTLOCALMAX(F,THRESH) accepts an element as a mazimizer only
-%   if it is at least THRES greater than all its neighbors.
-%
-%   SEL=SIFTLOCALMAX(F,THRESH,P) look for neighbors only in the first
-%   P dimensions of the Q-dimensional array F. This is useful to
-%   process F in ``slices''.
-%
-%   REMARK.  Matrices (2-array) with a singleton dimension are
-%   interpreted as vectors (1-array). So for example SIFTLOCALMAX([0 1
-%   0]) and SIFTLOCALMAX([0 1 0]') both return 2 as an aswer. However,
-%   if [0 1 0] is to be interpreted as a 1x2 matrix, then the correct
-%   answer is the empty set, as all elements are on the boundary.
-%   Unfortunately MATLAB does not distinguish between vectors and
-%   2-matrices with a singleton dimension.  To forece the
-%   interpretation of all matrices as 2-arrays, use
-%   SIFTLOCALMAX(F,TRESH,2) (but note that in this case the result is
-%   always empty!).