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Yongyi Zhao

dblp:95/4279 · DBLP profile ↗
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2ranked-venue papers
1as first author
2since 2021 · last 2022
0000-0002-4242-6910ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 1 first-author · 2 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Medical and health informatics · 100%
Artificial intelligence
1 paper
3D vision · 100%
Computer graphics and multimedia
2 papers
Image and video processing · 64% Computational photography and imaging · 36%

Topics — the 5 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision
neural radiance field
0.612022
PANDORA: Polarization-Aided Neural Decomposition of Radiance · ECCV (7) 2022
Medical and health informatics › medical imaging
diffuse optical tomography
0.512021
High Resolution, Deep Imaging Using Confocal Time-of-Flight Diffuse Optical Tomography · IEEE Trans. Pattern Anal. Mach. Intell. 2021
Computational photography and imaging
polarization imaging
0.212022
PANDORA: Polarization-Aided Neural Decomposition of Radiance · ECCV (7) 2022
Image and video processing › image reconstruction
fast reconstruction
0.112021
High Resolution, Deep Imaging Using Confocal Time-of-Flight Diffuse Optical Tomography · IEEE Trans. Pattern Anal. Mach. Intell. 2021
Image and video processing
image reconstruction
0.112021
High Resolution, Deep Imaging Using Confocal Time-of-Flight Diffuse Optical Tomography · IEEE Trans. Pattern Anal. Mach. Intell. 2021

Methods — techniques the papers use, named apart from their topics

polarization decomposition · 1.1neural radiance field · 1.1illumination multiplexing · 1.0convolutional approximation · 1.0confocal measurement · 1.0
YearPublicationVenuePosition
2022 PANDORA: Polarization-Aided Neural Decomposition of Radiance
Akshat Dave, Yongyi Zhao, Ashok Veeraraghavan
ECCV (7)2
2021 High Resolution, Deep Imaging Using Confocal Time-of-Flight Diffuse Optical Tomography
abstract
Light scattering by tissue severely limits how deep beneath the surface one can image, and the spatial resolution one can obtain from these images. Diffuse optical tomography (DOT) is one of the most powerful techniques for imaging deep within tissue - well beyond the conventional ∼ 10-15 mean scattering lengths tolerated by ballistic imaging techniques such as confocal and two-photon microscopy. Unfortunately, existing DOT systems are limited, achieving only centimeter-scale resolution. Furthermore, they suffer from slow acquisition times and slow reconstruction speeds making real-time imaging infeasible. We show that time-of-flight diffuse optical tomography (ToF-DOT) and its confocal variant (CToF-DOT), by exploiting the photon travel time information, allow us to achieve millimeter spatial resolution in the highly scattered diffusion regime ( mean free paths). In addition, we demonstrate two additional innovations: focusing on confocal measurements, and multiplexing the illumination sources allow us to significantly reduce the measurement acquisition time. Finally, we rely on a novel convolutional approximation that allows us to develop a fast reconstruction algorithm, achieving a 100× speedup in reconstruction time compared to traditional DOT reconstruction techniques. Together, we believe that these technical advances serve as the first step towards real-time, millimeter resolution, deep tissue imaging using DOT.
Yongyi Zhao, Ankit Raghuram, Hyun Keol Kim, Andreas H. Hielscher, Jacob T. Robinson, Ashok Veeraraghavan
IEEE Trans. Pattern Anal. Mach. Intell.1