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SrvfNet: A Generative Network for Unsupervised Multiple Diffeomorphic Shape Alignment

2021-04-27 19:49:46
Elvis Nunez, Andrew Lizarraga, Shantanu H. Joshi
arXiv_CV
         
Abstract

We present SrvfNet, a generative deep learning framework for the joint multiple alignment of large collections of functional data comprising square-root velocity functions (SRVF) to their templates. Our proposed framework is fully unsupervised and is capable of aligning to a predefined template as well as jointly predicting an optimal template from data while simultaneously achieving alignment. Our network is constructed as a generative encoder-decoder architecture comprising fully-connected layers capable of producing a distribution space of the warping functions. We demonstrate the strength of our framework by validating it on synthetic data as well as diffusion profiles from magnetic resonance imaging (MRI) data.

Abstract (translated)

URL

https://arxiv.org/abs/2104.13449

PDF

https://arxiv.org/pdf/2104.13449.pdf

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