On 10/29/2013 5:11 AM, Olivier Grisel wrote:
2013/10/23 j vickroy :
On 10/23/2013 10:18 AM, Andreas Mueller wrote:
FYI, the features I would use for the superpixel based approach would be
"color" histogramms (bag of words of channel intensities):
Reshape the images to (-1, 6) so you have lists
Some of these
> techniques may be available in scikit-image (http://scikit-image.org/).
>
> Multi-spectral image classification is something a friend of mine has been
> working on for a project - I will ask for some additional tips. This sounds
> like really cool stuff! If you end up wit
t we are not completely satisfied
with its performance and it is difficult/tedious to train.
I am starting to look for alternative approaches, but I am really a
complete novice at this. This is my first foray into ML; nevertheless,
it is very interesting to me!
-- jv
On 10/22/2013 02:3
On 10/22/2013 4:54 PM, Ankit Agrawal wrote:
Hi Jim,
What Joe said is correct when you want to label/classify images, since
classifying images by trying to find similarity of the test image with the
training images on pixel level would not work even if there is some ordinary
geometric
-- just what I need! --jv
Joe
On 22 Oct 2013, at 22:27, jim vickroy wrote:
On 10/22/2013 2:47 PM, Joseph Jacobs wrote:
Hey Jim,
From my (non-expert) perspective, performing classification
pixel-wise would not be ideal (please correct me if I am wrong). I
think the better way would be to
Oct 2013, at 22:27, jim vickroy wrote:
On 10/22/2013 2:47 PM, Joseph Jacobs wrote:
Hey Jim,
From my (non-expert) perspective, performing classification
pixel-wise would not be ideal (please correct me if I am wrong). I
think the better way would be to perform some sort of feature
extraction on
l-by-pixel classification would not seem to scale well. --jv
Not sure how helpful that was.
Joe
On 22 Oct 2013, at 21:10, jim vickroy wrote:
Hi,
Apologies if this is an inappropriate question for this forum.
I have a collection of (1024x1024) mono-chromatic images in which
each pixe
Hi,
Apologies if this is an inappropriate question for this forum.
I have a collection of (1024x1024) mono-chromatic images in which each
pixel is to be labeled as 1 of several categories (e.g., 10).
Furthermore, each mono-chromatic image was captured through several
filters (e.g., 5).
My
]])
>>> a = numpy.asfortranarray(x)
>>> a.flags
C_CONTIGUOUS : False
F_CONTIGUOUS : True
OWNDATA : True
WRITEABLE : True
ALIGNED : True
UPDATEIFCOPY : False
>>>
>
> On 2 August 2012 15:23, Jim Vickroy wrote:
>> On 8/2/2012 6:05 AM,
On 8/2/2012 6:05 AM, Brian Holt wrote:
> Hi list,
>
> I'm refactoring the tree module to introduce lazy argsorting and my
> unit tests are failing with:
>
> Exception ValueError: ValueError(u'ndarray is not Fortran
> contiguous',) in 'sklearn.tree._tree.Tree.recursive_partition' ignored
>
> I
sumption.
-- jv
>
> On Fri, Jul 27, 2012 at 11:29:53AM -0600, Jim Vickroy wrote:
>> Hi,
>> I recently discovered scikit-learn and it looks very impressive!
>> I have a project that may be able to make use of scikit-learn and help
>> me dispense with allot of custo
Hi,
I recently discovered scikit-learn and it looks very impressive!
I have a project that may be able to make use of scikit-learn and help
me dispense with allot of custom code.
The task is to identify 8 categories of features on 1024x1024 Solar
images captured in 6 channels (wavelengths). A
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