Histograms of Oriented Gradients
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"):
Cita come
Leo (2024). Histograms of Oriented Gradients (https://www.mathworks.com/matlabcentral/fileexchange/33863-histograms-of-oriented-gradients), MATLAB Central File Exchange. Recuperato .
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- Image Processing and Computer Vision > Computer Vision Toolbox > Recognition, Object Detection, and Semantic Segmentation >
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