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Kazutaka Mitobe

dblp:64/7556 · DBLP profile ↗
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3ranked-venue papers
1as first author
0since 2021 · last 2014
0000-0001-8446-9567ORCID · corroborated

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

Graphics, computer vision, multimedia, augmented reality and games · 2 · 1 first-authorHuman-computer interaction and ubiquitous computing · 2 · 1 first-authorApplied, interdisciplinary, general and emerging 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.

Human-computer interaction and pervasive computing
1 paper
Learning and educational technologies · 77% Wearable and physiological sensing · 23%
Computer graphics and multimedia
1 paper
Computer animation and physical simulation · 77% Image and video processing · 23%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Learning and educational technologies
skill acquisition		 0.112012
Development of a ubiquitous learning system for dexterous hand operation · ISMAR 2012
Computer animation and physical simulation › motion capture
motion capture data processing		 0.112010
Derivation of dance similarity from balance characteristics · SIGGRAPH ASIA (Sketches) 2010
Image and video processing › motion analysis
human motion analysis		 0.012010
Derivation of dance similarity from balance characteristics · SIGGRAPH ASIA (Sketches) 2010

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

motion capture · 0.1balance characteristic extraction · 0.1
YearPublicationVenuePosition
2014 Gaps between the Expectations of People with Hearing Impairment toward Subtitles and the Current Conditions for Subtitle Creation in Japan
Sawako Nakajima, Naoyuki Okochi, Kazutaka Mitobe, Tetsujiro Yamagami
 ICCHP (1)3
2012 Development of a ubiquitous learning system for dexterous hand operation
abstract
It is not easy to learn the dexterous finger movements of a skilled person due to flexibility and complexity. Therefore, a lot of training and effort is needed for a novice in order to acquire proficiency. The role model and feedback information of experienced persons is important for learning skilled movements. In this paper, we evaluate a ubiquitous learning system for dexterous hand operation using the finger motion capture data measured by the Hand-MoCap system. Through this system, a novice can learn under the guidance of the virtual 3D hand reconstructed using the master's motion capture data. We quantitatively evaluate the effectiveness of the system.
Kazutaka Mitobe, Masahiro Tomioka, Masachika Saito, Masafumi Suzuki
 ISMAR1
2010 Derivation of dance similarity from balance characteristics
abstract
It has been recognized that the design of similarity measures to compare multiple motion-capture (mocap) data streams is one of the major tasks in human motion analysis [Müller et al. 2008]. The information of similarity is utilized for many applications such as the retrieval of mocap data streams.
Takeshi Miura, Kazutaka Mitobe, Takaaki Kaiga, Takashi Yukawa, Katsubumi Tajima, Hideo Tamamoto
 SIGGRAPH ASIA (Sketches)2