skolouri / BAMC2019

These slides were presented in BAMC 2019

BAMC2019

This repository contains the slides I presented at BAMC2019. The Python package for transport-based transforms along side many tutorials can be found here:

https://github.com/LiamCattell/optimaltransport

For additional information see our project's webpage:

http://imagedatascience.com/transport/tutorials_isbi18.html

References:

• Overview 1: Kolouri, S., Park, S.R., Thorpe, M., Slepcev, D. and Rohde, G.K., 2017. Optimal mass transport: Signal processing and machine-learning applications. IEEE signal processing magazine, 34(4), pp.43-59.

• Overview 2: Cattell, L. and Rohde, G.K., 2018. Transport-Based Pattern Theory: A Signal Transformation Approach. arXiv preprint arXiv:1802.07163.

• CDT: Park, S.R., Kolouri, S., Kundu, S. and Rohde, G.K., 2018. The cumulative distribution transform and linear pattern classification. Applied and Computational Harmonic Analysis, 45(3), pp.616-641.

• Radon-CDT: Kolouri, S., Park, S.R. and Rohde, G.K., 2016. The radon cumulative distribution transform and its application to image classification. IEEE transactions on image processing, 25(2), pp.920-934.

• LOT (Kantorovich): Wang, W., Slepčev, D., Basu, S., Ozolek, J.A. and Rohde, G.K., 2013. A linear optimal transportation framework for quantifying and visualizing variations in sets of images. International journal of computer vision, 101(2), pp.254-269.

• LOT (Monge): Kolouri, S., Tosun, A.B., Ozolek, J.A. and Rohde, G.K., 2016. A continuous linear optimal transport approach for pattern analysis in image datasets. Pattern recognition, 51, pp.453-462.

• LOT (Variational 3D): Kundu, S., Kolouri, S., Erickson, K.I., Kramer, A.F., McAuley, E. and Rohde, G.K., 2018. Discovery and visualization of structural biomarkers from MRI using transport-based morphometry. NeuroImage, 167, pp.256-275.

• Super Resolution: Kolouri, S. and Rohde, G.K., 2015. Transport-based single frame super resolution of very low resolution face images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 4876-4884).

• Inverse Problem: Cattell, L., Meyer, C.H., Epstein, F.H. and Rohde, G.K., 2017, October. Reconstructing high-resolution cardiac MR movies from under-sampled frames. In 2017 51st Asilomar Conference on Signals, Systems, and Computers (pp. 1255-1259). IEEE.