I have been working on a open source face recognition demo tool called
FaceL for the past few months. FaceL is a simple and fun face
processing and labeling tool that labels faces in a live video from an
iSight camera or webcam. It uses OpenCV for face detection, ASEF
correlation
I have implemented an iterative gaussian smoothing approach that is
working well for my purposes. My approach uses a median filter to
populate the initial values and then runs a few passes with gaussian
smoothing. This works very well for the missing values that I care
about within the
Thanks for all the ideas. I think I will look into the
scikits.delaunay, Rbf, or gaussian smoothing approach. My best idea
is similar to the Gaussian smoothing. Anyway, all of the missing data
gaps seem to be small enough that I expect any of these methods to
accomplish my purpose.
I am working on a face recognition using 3D data from a special 3D
imaging system. For those interested the data comes from the FRGC
2004 dataset. The problem I am having is that for some pixels the
scanner fails to capture depth information. The result is that the
image has missing
I have written up basic nearest neighbor algorithm. It does a brute
force search so it will be slower than kdtrees as the number of points
gets large. It should however work well for high dimensional data. I
have also added the option for user defined distance measures. The
user can
I remember reading a paper or book that stated that for data that has
been normalized correlation and Euclidean are equivalent and will
produce the same knn results. To this end I spent a couple hours this
afternoon doing the math. This document is the result.
I also like the idea of a scipy.spatial library. For the research I
do in machine learning and computer vision we are often interested in
specifying different distance measures. It would be nice to have a
way to specify the distance measure. I would like to see a standard
set included:
be willing to
implement an exhaustive search KNN that would support user defined
functions.
On Oct 2, 2008, at 2:01 PM, Matthieu Brucher wrote:
2008/10/2 David Bolme [EMAIL PROTECTED]:
I also like the idea of a scipy.spatial library. For the research I
do in machine learning and computer
I am building up a python Computer Vision / Image Processing library
built on PIL/Scipy. If you cannot find a home for this code in scipy
I think it would be a good fit for this library.
http://pyvision.sourceforge.net
Dave
On Aug 3, 2008, at 3:26 AM, Nadav Horesh wrote:
My main
#551: numpy.ndarray messed up after unpickling
I have added a comment to this one. I don't think it should be
closed. I think there is a problem with initialization of the
ndarray. The normal constructor works fine, loading from pickle does
not.
. The valgrind
output is from a run where the code did not crash but valgrind still
detected many errors.
On Jan 26, 2008, at 3:01 PM, David Bolme wrote:
I think you are right. This does seem to be the same bug as 551. I
will try a non optimized ATLAS to see if that helps
I think you are right. This does seem to be the same bug as 551. I
will try a non optimized ATLAS to see if that helps.
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A am having some trouble when pickling numpy arrays. Basically I use
one python script to create the array and then pickle it. When I load
the pickled array using a different python script it appears to load
fine. When I try to perform a matrix multiply on the array with a
vector (using
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