Pattern and object recognition with COSFIRE

[George Azzopardi]

Dear all, 

I am pleased to see interest in the COSFIRE approach that I started during 
my PhD studies.

The COSFIRE approach is a trainable pattern recognition approach which can 
be applied to several applications, including feature detection, object 
recognition and localization, image classification, contour detection and 
vessel segmentation. The selectively for a pattern of interest is 
automatically configured in a training process. The method involves several 
computations that are independent of each other, and thus it can be easily 
implemented using parallel programming (e.g. on a GPU). The original paper 
(http://www.cs.rug.nl/~george/articles/PAMI2013.pdf) combines information 
about the contours of the concerned pattern. We now have another paper 
which is currently being reviewed for CVPR2015 where we show that by adding 
colour information COSFIRE filters become even more robust.

Please feel free to send me other ideas on how this work can be developed 
further.

I would be very happy and available to work with an undergraduate or a 
postgraduate student (or any other person) to have this parallel 
implementation in Python. I see that you already added it to the 
Requested-features page. You can also add my contact details (geazzo at gmail) 
there for the interested readers.

All my papers can be freely downloaded from my 
website: http://www.cs.rug.nl/~george/research-activities/

regards,
George

On Tuesday, 16 December 2014 15:22:45 UTC+1, Stefan van der Walt wrote:
>
> On Tue, Dec 16, 2014 at 1:57 PM, Pratap Vardhan <prat... at gmail.com 
> <javascript:>> wrote: 
> > I found few copies of the paper hosted by universities. I haven't 
> checked if 
> > these are the actual pre-prints - However, by the citation it looks like 
> it. 
>
> Thanks!  I've added it to the list: 
>
> https://github.com/scikit-image/scikit-image/wiki/Requested-features 
>
> Stéfan 
>
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