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Stephen B. Webb

dblp:36/2146 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2004
—ORCID · none

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

Graphics, computer vision, multimedia, augmented reality and games · 1Human-computer interaction and ubiquitous computing · 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
Virtual and augmented reality · 61% Image and video processing · 30% Computational photography and imaging · 9%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Virtual and augmented reality
immersive display
0.012004
Camera-Based Detection and Removal of Shadows from Interactive Multiprojector Displays · IEEE Trans. Vis. Comput. Graph. 2004
Virtual and augmented reality › immersive display
multi-projector display
0.012004
Camera-Based Detection and Removal of Shadows from Interactive Multiprojector Displays · IEEE Trans. Vis. Comput. Graph. 2004
Image and video processing › image enhancement
shadow detection and removal
0.012004
Camera-Based Detection and Removal of Shadows from Interactive Multiprojector Displays · IEEE Trans. Vis. Comput. Graph. 2004
Computational photography and imaging › projector-camera systems
projector calibration
0.012004
Camera-Based Detection and Removal of Shadows from Interactive Multiprojector Displays · IEEE Trans. Vis. Comput. Graph. 2004

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

pixel value adjustment · 0.0camera-based detection · 0.0
YearPublicationVenuePosition
2004 Camera-Based Detection and Removal of Shadows from Interactive Multiprojector Displays
abstract
Front-projection displays are a cost-effective and increasingly popular method for large format visualization and immersive rendering of virtual models. New approaches to projector tiling, automatic calibration, and color balancing have made multiprojector display systems feasible without undue infrastructure changes and maintenance. As a result, front-projection displays are being used to generate seamless, visually immersive worlds for virtual reality and visualization applications with reasonable cost and maintenance overhead. However, these systems suffer from a fundamental problem: Users and other objects in the environment can easily and inadvertently block projectors, creating shadows on the displayed image. Shadows occlude potentially important information and detract from the sense of presence an immersive display may have conveyed. We introduce a technique that detects and corrects shadows in a multiprojector display while it is in use. Cameras observe the display and compare observations with an expected image to detect shadowed regions. These regions are transformed to the appropriate projector frames, where corresponding pixel values are increased and/or attenuated. In display regions where more than one projector contributes to the image, shadow regions are eliminated.
Christopher O. Jaynes, Stephen B. Webb, R. Matt Steele
IEEE Trans. Vis. Comput. Graph.2
2001 Dynamic Shadow Removal from Front Projection Displays
abstract
Front-projection display environments suffer from a fundamental problem: users and other objects in the environment can easily and inadvertently block projectors, creating shadows on the displayed image. We introduce a technique that detects and corrects transient shadows in a multi-projector display. Our approach is to minimize the difference between predicted (generated) and observed (camera) images by continuous modification of the projected image values for each display device. We speculate that the general predictive monitoring framework introduced here is capable of addressing more general radiometric consistency problems. Using an automatically-derived relative position of cameras and projectors in the display environment and a straightforward color correction scheme, the system renders an expected image for each camera location. Cameras observe the displayed image, which is compared with the expected image to detect shadowed regions. These regions are transformed to the appropriate projector frames, where corresponding pixel values are increased. In display regions where more than one projector contributes to the image, shadow regions are eliminated. We demonstrate an implementation of the technique in a multiprojector system.
Christopher O. Jaynes, Stephen B. Webb, R. Matt Steele, Michael S. Brown, W. Brent Seales
IEEE Visualization2