The DeepLearningEpilepsyDetectionChallenge: design, implementation, andtestofanewcrowdsourced AIchallengeecosystem Isabell Kiral*, Subhrajit Roy*, Todd Mummert*, Alan Braz*, Jason Tsay, Jianbin Tang, Umar Asif, Thomas Schaffter, Eren Mehmet, The IBM Epilepsy Consortium◊ , Joseph Picone, Iyad Obeid, Bruno De Assis Marques, Stefan Maetschke, Rania Khalaf†, Michal RosenZvi† , Gustavo Stolovitzky† , Mahtab Mirmomeni† , Stefan Harrer† * These authors contributed equally to this work † Corresponding authors: rkhalaf@us.ibm.com, rosen@il.ibm.com, gustavo@us.ibm.com, mahtabm@au1.ibm.com, sharrer@au.ibm.com ◊ Members of the IBM Epilepsy Consortium are listed in the Acknowledgements section J. Picone and I. Obeid are with Temple University, USA. T. Schaffter is with Sage Bionetworks, USA. E. Mehmet is with the University of Illinois at UrbanaChampaign, USA. All other authors are with IBM Research in USA, Israel and Australia. Introduction This decade has seen an evergrowing number of scientific fields benefitting from the advances in machine learning technology and tooling. More recently, this trend reached the medical domain, with applications reaching from cancer diagnosis [1] to the development of brainmachineinterfaces [2]. While Kaggle has pioneered the crowdsourcing of machine learning challenges to incentivise data scientists from around the world to advance algorithm and model design, the increasing complexity of problem statements demands of participants to be expert datamore »
This content will become publicly available on June 20, 2023
On the Instability of Relative Pose Estimation and RANSAC's Role
Relative pose estimation using the 5point or 7point Random Sample Consensus (RANSAC) algorithms can fail even when no outliers are present and there are enough inliers to support a hypothesis. These cases arise due to numerical instability of the 5 and 7point minimal problems. This paper characterizes these instabilities, both in terms of minimal world scene configurations that lead to infinite condition number in epipolar estimation, and also in terms of the related minimal image feature pair correspondence configurations. The instability is studied in the context of a novel framework for analyzing the conditioning of minimal problems in multiview geometry, based on Riemannian manifolds. Experiments with synthetic and realworld data reveal that RANSAC does not only serve to filter out outliers, but RANSAC also selects for wellconditioned image data, sufficiently separated from the illposed locus that our theory predicts. These findings suggest that, in future work, one could try to accelerate and increase the success of RANSAC by testing only wellconditioned image data.
 Award ID(s):
 1910530
 Publication Date:
 NSFPAR ID:
 10333122
 Journal Name:
 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
 Page Range or eLocationID:
 89358943
 ISSN:
 2332564X
 Sponsoring Org:
 National Science Foundation
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