If you are interested I now have the code on source forge. It still
needs some critical documentation. I am planing to get this
documented and a beta release sometime this summer. Currently it ties
together PIL, OpenCV, numpy/scipy, LibSVM, and some of own code with
an emphases on face re
Am Dienstag, 11. März 2008 00:24:04 schrieb David Bolme:
> The steps you describe here are correct. I am putting together an
> open source computer vision library based on numpy/scipy. It will
> include an automatic PCA algorithm with face detection, eye detection,
> PCA dimensionally reduction,
The steps you describe here are correct. I am putting together an
open source computer vision library based on numpy/scipy. It will
include an automatic PCA algorithm with face detection, eye detection,
PCA dimensionally reduction, and distance measurement. If you are
interested let me k
I think that is correct...
Here is what the final result should look like:
http://www.datawrangling.com/media/images/first_16.png
If the dimensions for the sample faces don't work out to ( 361 x 361 ) in
the end, then you are likely to be missing a transpose somewhere. Also, be
aware that the s
On Mar 1, 12:57 am, "Peter Skomoroch" wrote:
I think
> > matlab example should be easy to translate to scipy/matplotlib using the
> > montage function:
>
> > load faces.mat
> > %Form covariance matrix
> > C=cov(faces');
> > %build eigenvectors and eigenvalues
> > [E,D] = eig(C);
hi Peter,
nice
Forgot the url:
http://www.cis.hut.fi/Opinnot/T-61.2010/harjoitustyo_en07.shtml
On Fri, Feb 29, 2008 at 2:56 PM, Peter Skomoroch <[EMAIL PROTECTED]>
wrote:
> Here is the page I referenced for the octave version ... it includes
> examples very similar to what you want. I will be posting a very s
Here is the page I referenced for the octave version ... it includes
examples very similar to what you want. I will be posting a very similar
example in Python later this month.
I don't have any Python code on hand for the Petland paper, but I think
matlab example should be easy to translate to s
RoyG,
The timing of your question couldn't be better, I just did an blog post on
this (I also plugged scipy and the EPD):
http://www.datawrangling.com/python-montage-code-for-displaying-arrays.html
The code basically replicates the matlab montage() function and approach to
handling grayscale ima
hi guys
I have a set of face images with which i want to do face recognition
using Petland's PCA method.I gathered these steps from their docs
1.represent matrix of face images data
2.find the adjusted matrix by substracting the mean face
3.calculate covariance matrix (cov=A* A_transpose) where
