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Joseph Ford

dblp:208/4226 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2017
—ORCID · none

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

Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 1

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.

Computer graphics and multimedia
1 paper
Computational photography and imaging · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational photography and imaging
depth estimation
0.312017
A Wide-Field-of-View Monocentric Light Field Camera · CVPR 2017
Computational photography and imaging
light field imaging
0.312017
A Wide-Field-of-View Monocentric Light Field Camera · CVPR 2017
Computational photography and imaging › image acquisition › imaging system design
camera design
0.112017
A Wide-Field-of-View Monocentric Light Field Camera · CVPR 2017

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

spherical parameterization · 0.3monocentric lens · 0.3microlens array · 0.3
YearPublicationVenuePosition
2017 A Wide-Field-of-View Monocentric Light Field Camera
abstract
Light field (LF) capture and processing are important in an expanding range of computer vision applications, offering rich textural and depth information and simplification of conventionally complex tasks. Although LF cameras are commercially available, no existing device offers wide field-of-view (FOV) imaging. This is due in part to the limitations of fisheye lenses, for which a fundamentally constrained entrance pupil diameter severely limits depth sensitivity. In this work we describe a novel, compact optical design that couples a monocentric lens with multiple sensors using microlens arrays, allowing LF capture with an unprecedented FOV. Leveraging capabilities of the LF representation, we propose a novel method for efficiently coupling the spherical lens and planar sensors, replacing expensive and bulky fiber bundles. We construct a single-sensor LF camera prototype, rotating the sensor relative to a fixed main lens to emulate a wide-FOV multi-sensor scenario. Finally, we describe a processing toolchain, including a convenient spherical LF parameterization, and demonstrate depth estimation and post-capture refocus for indoor and outdoor panoramas with 15 × 15 × 1600 × 200 pixels (72 MPix) and a 138° FOV.
Donald G. Dansereau, Glenn Schuster, Joseph Ford, Gordon Wetzstein
CVPR3