Peekaboo

Recently some segmentation and superpixel algorithms I implemented were merged into scikits-image . You can see the example here . I reimplemented Felzenszwalb's fast graph based method , quickshift and SLIC . The goal was to have easy access to some successful methods to make comparison easier and encourage experimenting with the algorithms. Here is a a comparison of my implementations against the original implementations on Lena (downscaled by a factor of 2). The first row is my implementation, the second the original.

From a discussion on the scikits.learn mailing list I stumbled across scikits.image . How great is that? I have been looking for something like that for quite a while now: A low to mid-level image library for python. It is still in a very early stage at the moment and mostly consists of io, morphological operations and opencv function as far as I can see. But it can convert colorspaces! I don't know how many times I thought "why?!!? why do I have to write my RBG to HSV myself?!" This makes me ever so happy :) I hope I find the time to contribute and I hope this project will gain more momentum. [edit] As an afterthought, I'd like to quote the following message from Nicolas Pinto on the scikits-learn mailing list: [about histogram of oriented gradients] I believe this should be part of scikits.image along with sift, phog, bow, phow, geometric blur, gabor jets, etc. We had planned to integrate them all but it felt through the cracks. I hope that the scik...