Hello Juan By a labeled region I am guessing you mean a 2d array in which all pixels within the blob are set to a value ? That would involve extra computation in this case. The algorithms mention ( at least DoG and LoG ) do not compute the pixels belonging to the blob, How about returning a list of tuples of (x,y,area). Than should be generic enough I guess Thanks Vighnesh On Thursday, February 27, 2014 7:32:26 PM UTC+5:30, Juan Nunez-Iglesias wrote:

Hi Vighnesh,

I'm not familiar with blob detection algorithms, but I will say that in general in scikit-image we try to shy away from new classes, and prefer to represent things with numpy arrays wherever possible. I think an integer image with one labeled region per blob would be appropriate (and could be piped into regionprops).

Juan.

On Thu, Feb 27, 2014 at 5:41 PM, Vighnesh Birodkar <vighnesh...@gmail.com<javascript:>

...

wrote:

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Hello

I am applying as a GSoC student for scikit-image this year. Last year I was a GSoC student for SimpleCV. To start contributing I wanted to implement Blob Detection ( DOG and LOG initially ).

I wanted to have some guidelines as to where to put the code and how to structure it.Is there any piece of code lying around in scikit-image which I can use as a template for Blob Detection ?

I would put the new code inside a new .py file inside features. I think it would be a good idea to keep the prototypes identical for all blob detection algorithms. My first intuition is to return a class ( or a dictionary ) with some attributes set. Some blob detection algorithms may compute the area of the blobs as well while some might not. Or I can just return the (x,y) coordinates.

Please point out if I am mistaken somewhere.

Thanks Vighnesh

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