What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Operating system
- Microsoft Windows
His primary scientific interests are in Artificial intelligence, Human–computer interaction, Computer vision, Computer graphics and Interface.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Tensor field and Fitts's law.
His studies deal with areas such as User interface, Interaction technique and Multimedia as well as Human–computer interaction.
His work on Cursor as part of general Computer vision study is frequently linked to Constraint graph, bridging the gap between disciplines.
Takeo Igarashi studies Computer graphics, namely 3D modeling.
His study in Interface is interdisciplinary in nature, drawing from both Virtual reality, State, Sketch-based modeling and SAFER.
His most cited work include:
- Teddy: a sketching interface for 3D freeform design (1138 citations)
- As-rigid-as-possible shape manipulation (592 citations)
- FiberMesh: designing freeform surfaces with 3D curves (308 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Human–computer interaction, Computer graphics, Computer vision and User interface.
His Artificial intelligence research incorporates themes from Machine learning, Process and Pattern recognition.
His Human–computer interaction study incorporates themes from Robot, Human–robot interaction, Graphical user interface, Multimedia and Interface.
His Computer graphics research is multidisciplinary, relying on both Sketch, Interface and Sketch-based modeling.
A large part of his Computer vision studies is devoted to Object.
His research ties Set and User interface together.
He most often published in these fields:
- Artificial intelligence (27.60%)
- Human–computer interaction (26.96%)
- Computer graphics (26.96%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (27.60%)
- Human–computer interaction (26.96%)
- Process (6.79%)
In recent papers he was focusing on the following fields of study:
Takeo Igarashi focuses on Artificial intelligence, Human–computer interaction, Process, Pattern recognition and User interface.
His Artificial intelligence research incorporates elements of Machine learning and Computer vision.
His Computer vision study integrates concerns from other disciplines, such as Animation, Control unit and Reference model.
His research integrates issues of Creative visualization, Interface and Reinforcement learning in his study of Human–computer interaction.
His biological study spans a wide range of topics, including Engineering drawing, Texture, Tree, Interface and Convolutional neural network.
His User interface research integrates issues from Set, Programming by example, Data mining and Ambiguity.
Between 2017 and 2021, his most popular works were:
- A Video-based Study Comparing Communication Modalities between an Autonomous Car and a Pedestrian (23 citations)
- Extended narrow band FLIP for liquid simulations (10 citations)
- CoreCavity: interactive shell decomposition for fabrication with two-piece rigid molds (9 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Operating system
- Microsoft Windows
His scientific interests lie mostly in Human–computer interaction, Artificial intelligence, Set, Animation and Interface.
His Human–computer interaction research includes themes of Video based and Reinforcement learning.
The concepts of his Artificial intelligence study are interwoven with issues in Sketch and Pattern recognition.
His work carried out in the field of Set brings together such families of science as Orientation, Robot and Computer graphics.
The various areas that Takeo Igarashi examines in his Animation study include 3d model, Volume, RGB color model, Computer vision and Skeleton.
His work in Interface addresses issues such as Molding, which are connected to fields such as Engineering drawing.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
