Computational 3D topographic microscopy from terabytes of data per sample

no code implementations • 5 Jun 2023 • Kevin C. Zhou, Mark Harfouche, Maxwell Zheng, Joakim Jönsson, Kyung Chul Lee, Ron Appel, Paul Reamey, Thomas Doman, Veton Saliu, Gregor Horstmeyer, Roarke Horstmeyer

We present a large-scale computational 3D topographic microscope that enables 6-gigapixel profilometric 3D imaging at micron-scale resolution across $>$110 cm$^2$ areas over multi-millimeter axial ranges.

3D Reconstruction

Imaging across multiple spatial scales with the multi-camera array microscope

no code implementations • 30 Nov 2022 • Mark Harfouche, Kanghyun Kim, Kevin C. Zhou, Pavan Chandra Konda, Sunanda Sharma, Eric E. Thomson, Colin Cooke, Shiqi Xu, Lucas Kreiss, Amey Chaware, Xi Yang, Xing Yao, Vinayak Pathak, Martin Bohlen, Ron Appel, Aurélien Bègue, Clare Cook, Jed Doman, John Efromson, Gregor Horstmeyer, Jaehee Park, Paul Reamey, Veton Saliu, Eva Naumann, Roarke Horstmeyer

This article experimentally examines different configurations of a novel multi-camera array microscope (MCAM) imaging technology.

Multi-element microscope optimization by a learned sensing network with composite physical layers

no code implementations • 27 Jun 2020 • Kanghyun Kim, Pavan Chandra Konda, Colin L. Cooke, Ron Appel, Roarke Horstmeyer

Standard microscopes offer a variety of settings to help improve the visibility of different specimens to the end microscope user.

Classification General Classification

A Simple Multi-Class Boosting Framework with Theoretical Guarantees and Empirical Proficiency

no code implementations • ICML 2017 • Ron Appel, Pietro Perona

There is a need for simple yet accurate white-box learning systems that train quickly and with little data.

Improved Multi-Class Cost-Sensitive Boosting via Estimation of the Minimum-Risk Class

no code implementations • 12 Jul 2016 • Ron Appel, Xavier Burgos-Artizzu, Pietro Perona

We present a simple unified framework for multi-class cost-sensitive boosting.