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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | % VL_HOG Compute HOG features
% HOG = VL_HOG(IM, CELLSIZE) computes the HOG features for image IM
% and the specified CELLSIZE. IM can be either grayscale or colour
% in SINGLE storage class. HOG is an array of cells: its number
% of columns is approximately the number of columns of IM divided
% by CELLSIZE and the same for the number of rows. The third
% dimension spans the feature compoents.
%
% PERM = VL_HOG('permutation') returns the left-right permutation
% to apply to each HOG cell to flip it.
%
% IMAGE = VL_HOG('render', HOG) returns an IMAGE containing an
% iconic representation of the array of cells HOG.
%
% Options:
%
% Variant:: 'UoCTTI'
% Choose a HOG variant: 'UoCTTI' or 'DalalTriggs'.
%
% NumOrientations:: 9
% Choose a number of undirected orientations in the orientation
% histograms. The angle [0,pi) is divided in to NumOrientation
% equal parts.
%
% DirectedPolarField::
% By specifying this flag the image IM is interpreted as samples
% from a 2D vector field specified by their argument IM(:,:,2) and
% modulus IM(:,:,1).
%
% UndirectedPolarField::
% Same as above, but wraps angles in [0,pi).
%
% BilinearOrientations::
% This flags activates the use of bilinear interpolation to assign
% orientations to bins. This produces a smoother feature, but is
% not some other implementations (e.g. UoCTTI).
%
% Example:: computing and visualizing HOG features
% hog = vl_hog(im2single(im)) ; % compute HOG features
% Author: Andrea Vedaldi
% Copyright (C) 2012-13 Andrea Vedaldi.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
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