robust ring (and circle) detection algorithm

[Juan Nunez-Iglesias]

Hi Eldad,


I'm interested in this but don't have the bandwidth to review this until next week at the earliest. If no one steps up before then I'll keep you posted!




Juan. 
—
Sent from Mailbox for iPhone

On Sun, Mar 9, 2014 at 11:05 PM, Eldad Afik <eldad.afik at gmail.com> wrote:

> Dear Devs, 
> In the process of tracking objects under the microscope I was confronted 
> with an image analysis problem, for which I could not find a satisfactory 
> solution in איק standard scientific packages (nor in the  literature survey 
> I made). 
> At the time I started this, scikit-image did not include a circle Hough 
> transform and OpenCV's was not good enough.
> I ended up developing an algorithm myself, which can be regarded as an 
> off-spring of the circle Hough transform. 
> I summarised the work in a manuscript titled:
>  "*Robust and highly performant ring detection algorithm for 3d particle 
> tracking using 2d microscope imaging*" (a pre-print is available at 
> arXiv:1310.1371 <http://arxiv.org/abs/1310.1371>).
> NB, it was written minded at potential users and applications rather than 
>  computer scientists, which I am not.
> I would like to make the code publicly available.
> I believe it may be beneficial to the scikit-image community and users as a 
> practical alternative to the current Standard Circle Hough transform.
> I contacted Stéfan van der Walt, who expressed interest in the algorithm.
> I would be grateful to have your feedback, any input would be helpful!
> With kind thanks to all contributors, 
> Eldad
> -- 
> Eldad Afik
> Physics of Complex Systems
> Weizmann Institute of Science
> *Some technical notes:*
> A* typical example *of the images I need to analyse can be found in the 
> manuscript arXiv:1310.1371 <http://arxiv.org/abs/1310.1371> (Fig. 1a as 
> well as S2a). 
> The *results* of the software I wrote exhibit a detection rate which 
> exceeds 94% and have only 1% false-detection, providing sub-pixel accuracy.
> The conceptual steps in making the algorithm* robust* are sketched in Fig. 
> 2.
> The main steps to make it* efficient*, both in execution-time as well as 
> memory consumption, are outlines on page 8; more technical details in this 
> respect can be found in the "Supporting Information" Text section.
> The "methods" section of the manuscript provides some details regarding the 
> *benchmarking*.
> -- 
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