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Wu Sa

dblp:91/7095 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2007
—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 architecture, parallel and distributed computing, and storage systems
1 paper
Performance modeling and evaluation · 100%
Human-computer interaction and pervasive computing
1 paper
Immersive interaction · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Performance modeling and evaluation
latency measurement
0.112007
Measurement of absolute latency for video see through augmented reality · ISMAR 2007
Immersive interaction
augmented reality interaction
0.012007
Measurement of absolute latency for video see through augmented reality · ISMAR 2007

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

projective transformation · 0.1cross ratio encoding · 0.1
YearPublicationVenuePosition
2007 Measurement of absolute latency for video see through augmented reality
abstract
Latency is a key property of video see through AR systems since users' performance is strongly related to it. However, there is no standard way of latency measurement of an AR system in the literature. We have created a stable and comparable way of estimating the latency in a video see through AR system. The latency is estimated by encoding the time in the image and decoding the time after camera feedback. We have encoded the time as a translation of a circle in the image. The cross ratio has been used as an image feature that is preserved in a projective transformation. The encoding allows for a simple but accurate way of decoding. We show that this way of encoding has an adequate accuracy for latency measurements. As the method allows for a series of automatic measurements we propose to visualize the measurements in a histogram. This histogram reveals meaningful information about the system other than the mean value and standard deviation of the latency. The method has been tested on four different AR systems that use different camera technology, resolution and frame rates.
Tobias Sielhorst, Wu Sa, Ali Kamen, Frank Sauer, Nassir Navab
ISMAR2