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Sydney Pang

dblp:36/8693 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2010
—ORCID · none

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

Graphics, 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.

Artificial intelligence
1 paper
Face, body and person analysis · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
GPUs and heterogeneous computing · 77% Embedded and real-time systems · 23%

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

TopicWeightPapersLastEvidence papers
Computer vision › Face, body and person analysis › face recognition
face annotation
0.112010
A GPU-accelerated face annotation system for smartphones · ACM Multimedia 2010
Computer vision › Face, body and person analysis
face recognition
0.112010
A GPU-accelerated face annotation system for smartphones · ACM Multimedia 2010
GPUs and heterogeneous computing › embedded GPU
embedded GPU acceleration
0.112010
A GPU-accelerated face annotation system for smartphones · ACM Multimedia 2010
Embedded and real-time systems › mobile computing
smartphone platform
0.012010
A GPU-accelerated face annotation system for smartphones · ACM Multimedia 2010

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

face recognition · 0.2GPU acceleration · 0.2
YearPublicationVenuePosition
2010 A GPU-accelerated face annotation system for smartphones
abstract
Face annotation makes it easy to share and manage digital photos and videos. While state-of-the-art face recognition algorithms can achieve high accuracy to support automatic face annotation, their implementations on an embedded platform cannot achieve real-time performance due to the demanding computational requirement. However, the availability of an embedded GPU in most smartphones offers the opportunity to use it as an accelerator for the face recognition task. In this demonstration, we show that, with acceleration achieved by the embedded low-power GPU, a real-time face annotation system could be realized on an existing off-the-shelf smartphone.
Yi-Chu Wang, Sydney Pang, Kwang-Ting Cheng
ACM Multimedia2