T
Takeo Kanade
Researcher at Carnegie Mellon University
Publications - 800
Citations - 107709
Takeo Kanade is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: Motion estimation & Image processing. The author has an hindex of 147, co-authored 799 publications receiving 103237 citations. Previous affiliations of Takeo Kanade include National Institute of Advanced Industrial Science and Technology & Hitachi.
Papers
More filters
Proceedings ArticleDOI
Modeling the product manifold of posture and motion
TL;DR: This paper proposes to model the nonlinear motion manifold as a collection of local linear models, noting that given a particular posture, the variation in motion for that posture can be well-approximated by a linear model.
Proceedings ArticleDOI
Active sample selection and correction propagation on a gradually-augmented graph
TL;DR: Experimental results conducted on three real world datasets validate that the active sample selection and correction propagation algorithm quickly reaches high quality classification results with minimal human interventions.
Proceedings ArticleDOI
Overlay what Humanoid Robot Perceives and Thinks to the Real-world by Mixed Reality System
Kazuhiko Kobayashi,Koichi Nishiwaki,Shinji Uchiyama,Hiroyuki Yamamoto,Satoshi Kagami,Takeo Kanade +5 more
TL;DR: A novel environment for robot development is presented, in which intermediate results of the system are overlaid on physical space using mixed reality technology, which gives a human-robot interface that shows the robot internal state intuitively, not only in development, but also in operation.
Proceedings ArticleDOI
Geometric invariants for verification in 3-D object tracking
U. Uenohara,Takeo Kanade +1 more
TL;DR: It is shown how geometric invariants from five coplanar points and affine moment invariants can be used for verification of feature tracking and Dynamic threshold setting is proposed for dealing with observation errors in tracking in any situation.
Proceedings ArticleDOI
Image matching with distinctive visual vocabulary
TL;DR: An image indexing and matching algorithm that relies on selecting distinctive high dimensional features that compares favorably with the state of the art in image matching tasks on the University of Kentucky Recognition Benchmark dataset and on an indoor localization dataset.