Histograms of Oriented Gradients

mex function for calculating histograms of (oriented) gradients (Dalal & Triggs CVPR 2005).

A mex function for calculating histograms of (oriented) gradients as described in the paper "Histograms of Oriented Gradients for Human Detection"[1]:

http://lear.inrialpes.fr/pubs/2005/DT05/

Function can be called with either one or two arguments :

hogs = HoG(Image,Params);
or
hogs = HoG(Image);

Params should be a size 5 vector with:

Params(0) = number of orientation bins.
Params(1) = cell size.
Params(2) = block size.
Params(3) = 1 for oriented gradients and 0 otherwise.
Params(4) = value for clipping of the L2-norm.

See [1] for more details on these values.

If the function is called with only one parameter then the default values are used:

Params = [9 8 2 0 0.2];

Function can be called for both a RGB and grayscale image.

The function only supports data of type double, image data should first be cast into double i.e. HoG(double(Image)).

Finally [1] mentions the possibility of downweighting "pixels near the edges
of the block by applying a Gaussian spatial window..." and that this leads to and increase in performance of 1% at 10^-4 FPPW. This downweighting scheme is not used by this function.

The HoG function code is part of the MASH public descriptors ("heuristics"):

https://cordis.europa.eu/project/rcn/93563_en.html