On 03/19/2013 01:55 PM, Fimi wrote:
Hi Brian,
I will look into this paper in more detail. Thank you for your reply.
If I have to use opencv or other wrappers like it that hide SVM behind
its interface I will not use it. The purpose of this small project is
to learn Support Vector Machines.
Su
From: Brian Holt
To: scikit-learn-general@lists.sourceforge.net
Sent: Tuesday, March 19, 2013 4:03:26 AM
Subject: Re: [Scikit-learn-general] Finding dimentions of faces on an image
As Gilles says, the scanning windows approach is pretty common for object (and
face) detection. Have
2013 3:49:27 AM
Subject: Re: [Scikit-learn-general] Finding dimentions of faces on an image
Hi,
Short answer: you cant.
Longer answer: If you use as training samples the whole images (with faces
somewhere in there), then your model is learning to discriminate between your 2
categories, from t
Hi Fimi.
Is there a reason you are not using the Viola-Jones implemented in OpenCV?
I should be available in SimpleCV, too, if you want a nice Python interface.
Cheers,
Andy
On 03/19/2013 05:19 AM, Fimi wrote:
Hello,
I've got non linear multiclass classification for support vector
machines to
As Gilles says, the scanning windows approach is pretty common for object
(and face) detection. Have you looked at the Viola Jones paper? It's the
standard for face detection and now that we have adaboost classifiers you
should be able to knock up an example quite quickly. Scikit Image might be
qui
Hi,
Short answer: you cant.
Longer answer: If you use as training samples the whole images (with faces
somewhere in there), then your model is learning to discriminate between
your 2 categories, from the whole images, with **no** information about
where the faces are actually located. As such, it
Hello,
I've got non linear multiclass classification for support vector machines to
work and it does predict the correct face and non face images. It has been a
very steep learning curve for me because this is the first time I do this type
of work.
I would like to see if you can guide me on a go
