Interesting to include a the Scattering transform in sci-kit image?

[Sira Ferradans]

Dear Sci-kit image developers, 


at the ENS (Paris) we are planning on implementing a Python version of the 
Scattering transform 2D and 1D. The scattering transform has proven to be 
very powerful as a descriptor for image classification and signal analysis. 
We thought that it may be useful to integrate the 2D version in the sci-kit 
image package, since it aligns well with the software package and the 
community. The idea would be to implement the functionalities following as 
much as possible the APIs you already have for similar functions. More 
specifically, we will need the following (approximately): 


                -Morlet wavelet: Closely related to the Gabor wavelet, so 
it should take into account its API
                -Scattering transform: output the scattering transform 
coefficients, either for display or (as a vector) for learning purposes. 


We are attaching a small tutorial (better visualized if you download it) 
that compares the performance of the first order scattering coefficients 
computed with Gabor filters, versus the coefficients you extract in the *'Gabor 
filter banks for texture classification' 
<http://scikit-image.org/docs/dev/auto_examples/features_detection/plot_gabor.html#example-features-detection-plot-gabor-py>* 
tutorial. The goal of this ipython notebook is to show that the 
implementation can be easily integrated in you library while providing a 
powerful tool for image analysis. 

If you think this is a good idea, please let us know. Moreover, it would be 
great if you could give us some guidelines that you think would make the 
process easier. We will be adhering to the instructions given on the 
contributions page, but please don't hesitate to give feedback on our PR! 


Best Regards, 

Michael Eickenberg and Sira Ferradans. 
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