Hello, I have one implementation question now: In order to implement the scattering, we need the wavelet transform, with morlet filters. I saw that scikit-image has support for pywt, but morlet filters are not supported now. This leaves the only option of implementing the wavelet transform directly. I thought that a good idea would be to define a list of list for all the filters, that would be saved in the fourier domain. In this context the convolution of the signal with the filter would be implemented as a multiplication in the Fourier domain. Is this implementation ok? do you have any comments, maybe references where to see a similar code already available in scikit-image? Thanks a lot for the possible comments and recommendations. On Tuesday, June 14, 2016 at 11:30:03 PM UTC+2, stefanv wrote:

Hi Sira, Michael

This looks very interesting! The papers you mention are well cited, and the classification results promising (I did some texture classification on rock slices during my masters, and even then it seemed like wavelets would come to dominate this domain).

I look forward to your PR & gallery example.

Thanks! Stéfan

On 13 June 2016 at 06:02, Sira Ferradans <sira.fe...@gmail.com <javascript:>> wrote:

...

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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