matlab fhog,fhog.m · wycjl/kcf_matlab_apce - Gitee.com

function H = fhog( I, binSize, nOrients, clip, crop )

% Efficiently compute Felzenszwalb's HOG (FHOG) features.

%

% A fast implementation of the HOG variant used by Felzenszwalb et al.

% in their work on discriminatively trained deformable part models.

% http://www.cs.berkeley.edu/~rbg/latent/index.html

% Gives nearly identical results to features.cc in code release version 5

% but runs 4x faster (over 125 fps on VGA color images).

%

% The computed HOG features are 3*nOrients+5 dimensional. There are

% 2*nOrients contrast sensitive orientation channels, nOrients contrast

% insensitive orientation channels, 4 texture channels and 1 all zeros

% channel (used as a 'truncation' feature). Using the standard value of

% nOrients=9 gives a 32 dimensional feature vector at each cell. This

% variant of HOG, refered to as FHOG, has been shown to achieve superior

% performance to the original HOG features. For details please refer to

% work by Felzenszwalb et al. (see link above).

%

% This function is essentially a wrapper for calls to gradientMag()

% and gradientHist(). Specifically, it is equivalent to the following:

% [M,O] = gradientMag( I,0,0,0,1 ); softBin = -1; useHog = 2;

% H = gradientHist(M,O,binSize,nOrients,softBin,useHog,clip);

% See gradientHist() for more general usage.

%

% This code requires SSE2 to compile and run (most modern Intel and AMD

% processors support SSE2). Please see: http://en.wikipedia.org/wiki/SSE2.

%

% USAGE

% H = fhog( I, [binSize], [nOrients], [clip], [crop] )

%

% INPUTS

% I - [hxw] color or grayscale input image (must have type single)

% binSize - [8] spatial bin size

% nOrients - [9] number of orientation bins

% clip - [.2] value at which to clip histogram bins

% crop - [0] if true crop boundaries

%

% OUTPUTS

% H - [h/binSize w/binSize nOrients*3+5] computed hog features

%

% EXAMPLE

% I=imResample(single(imread('peppers.png'))/255,[480 640]);

% tic, for i=1:100, H=fhog(I,8,9); end; disp(100/toc) % >125 fps

% figure(1); im(I); V=hogDraw(H,25,1); figure(2); im(V)

%

% EXAMPLE

% % comparison to features.cc (requires DPM code release version 5)

% I=imResample(single(imread('peppers.png'))/255,[480 640]); Id=double(I);

% tic, for i=1:100, H1=features(Id,8); end; disp(100/toc)

% tic, for i=1:100, H2=fhog(I,8,9,.2,1); end; disp(100/toc)

% figure(1); montage2(H1); figure(2); montage2(H2);

% D=abs(H1-H2); mean(D(:))

%

% See also hog, hogDraw, gradientHist

%

% Piotr's Image&Video Toolbox Version 3.23

% Copyright 2013 Piotr Dollar. [pdollar-at-caltech.edu]

% Please email me if you find bugs, or have suggestions or questions!

% Licensed under the Simplified BSD License [see external/bsd.txt]

%Note: modified to be more self-contained

if( nargin<2 ), binSize=8; end

if( nargin<3 ), nOrients=9; end

if( nargin<4 ), clip=.2; end

if( nargin<5 ), crop=0; end

softBin = -1; useHog = 2; b = binSize;

[M,O]=gradientMex('gradientMag',I,0,1);

H = gradientMex('gradientHist',M,O,binSize,nOrients,softBin,useHog,clip);

if( crop ), e=mod(size(I),b)

end

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