no code implementations • 18 Jul 2022 • Tal Amir, Shahar Kovalsky, Nadav Dym
Our relaxation enjoys several theoretical and practical advantages: Theoretically, we prove that our method provides a $\sqrt{2}$-factor approximation to the Robust Procrustes problem, and that, under appropriate assumptions, it exactly recovers the true rigid motion from point correspondences contaminated by outliers.
no code implementations • 29 Mar 2019 • David Dov, Shahar Kovalsky, Jonathan Cohen, Danielle Range, Ricardo Henao, Lawrence Carin
We consider preoperative prediction of thyroid cancer based on ultra-high-resolution whole-slide cytopathology images.
no code implementations • 28 Jul 2015 • Angjoo Kanazawa, Shahar Kovalsky, Ronen Basri, David W. Jacobs
In this paper, we show that such information can be learned from user-clicked 2D images and a template 3D model of the target animal.