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 ( 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@gmail) there for the interested readers.

All my papers can be freely downloaded from my website:


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 <> 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: