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Sung-Woo Lee

dblp:10/1735 · DBLP profile ↗
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6ranked-venue papers
4as first author
0since 2021 · last 2002
—ORCID · none

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

Systems, architecture and hardware · 4 · 3 first-authorTheory of computation · 2 · 1 first-authorArtificial intelligence and machine learning · 1 · 1 first-authorDatabases, data management, data science and information retrieval · 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
Video understanding and tracking · 100%
Computer graphics and multimedia
1 paper
Virtual and augmented reality · 100%

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

TopicWeightPapersLastEvidence papers
Computer vision › Video understanding and tracking
object tracking		 0.011999
Model-Based 3D Object Tracking Using Projective Invariance · ICRA 1999
Virtual and augmented reality › tracking
3d object tracking		 0.011999
Model-Based 3D Object Tracking Using Projective Invariance · ICRA 1999

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

visibility checking · 0.0projective invariance · 0.0corner tracking · 0.0
YearPublicationVenuePosition
2002 A Fully Compliant OpenMP Implementationon Software Distributed Shared Memory
Sven Karlsson, Sung-Woo Lee, Mats Brorsson
 HiPC2
2000 Using the Resizable Working-set Notion for Software Distributed Shared Memory
Sung-Woo Lee, Kee-Young Yoo
 CLUSTER1
2000 The Working-Set Based Adaptive Protocol for Software Distributed Shared Memory
Sung-Woo Lee, Kee-Young Yoo
 HiPC1
2000 Partitioned systolic architecture for modular multiplication in GF(2m)
Hyunsung Kim 0001, Sung-Woo Lee, Kee-Young Yoo
 Inf. Process. Lett.2
1999 Efficient Fixed-Size Systolic Arrays for the Modular Multiplication
Sung-Woo Lee, Hyunsung Kim 0001, Jung-Joon Kim, Tae-Geun Kim, Kee-Young Yoo
 COCOON1
1999 Model-Based 3D Object Tracking Using Projective Invariance
abstract
This paper describes a method of 3D object tracking by projective invariance. Usually, projective invariance has been used for object recognition and its property discards the need for cumbersome camera calibration to obtain metric information. We focus on the fact the projective invariance is not only regarded as recognition cues but also able to verify, the status of a 3D object for visual tracking. With projective invariance relevant to points, we develop a fast and reliable visual tracking strategy by combining a recognition module using projective invariance, window-based corner trackers for object's center tracking and a visibility checking module. The proposed method is experimented in single IBM-PC platform successfully.
Sung-Woo Lee, Bum-Jae You, Gregory D. Hager
 ICRA1