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Yoichi Motokawa

dblp:58/3336 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2006
—ORCID · none

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

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-authorHuman-computer interaction and ubiquitous computing · 1 · 1 first-author

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 · 100%
Human-computer interaction and pervasive computing
1 paper
Learning and educational technologies · 100%
Artificial intelligence
1 paper
3D vision · 100%

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

TopicWeightPapersLastEvidence papers
Virtual and augmented reality
augmented reality
0.112006
Support system for guitar playing using augmented reality display · ISMAR 2006
Virtual and augmented reality
visual guidance
0.112006
Support system for guitar playing using augmented reality display · ISMAR 2006
Learning and educational technologies › immersive learning
augmented reality learning
0.112006
Support system for guitar playing using augmented reality display · ISMAR 2006
Computer vision › 3D vision › pose estimation
pose tracking
0.012006
Support system for guitar playing using augmented reality display · ISMAR 2006

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

natural feature tracking · 0.2marker-based tracking · 0.2edge detection · 0.2
YearPublicationVenuePosition
2006 Support system for guitar playing using augmented reality display
abstract
Learning to play the guitar is difficult. We proposed a system that assists people learning to play the guitar using augmented reality. This system shows a learner how to correctly hold the strings by overlaying a virtual hand model and lines onto a real guitar. The player learning to play the guitar can easily understand the required position by overlapping their hand on a visual guide. An important issue for this system to address is the accurate registration between the visual guide and the guitar, therefore we need to track the pose and the position of the guitar. We also proposed a method to track the guitar with a visual marker and natural features of the guitar. Since we used marker information and edge information as natural features, the system could continually track the guitar. Accordingly, our system can constantly display visual guides at the required position to enable a player to learn to play the guitar in a natural manner.
Yoichi Motokawa, Hideo Saito 0001
ISMAR1