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
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Journal ArticleDOI
High-resolution terrain map from multiple sensor data
In So Kweon,Takeo Kanade +1 more
TL;DR: 3-D vision techniques for incrementally building an accurate 3-D representation of rugged terrain using multiple sensors and the locus method, which is used to estimate the vehicle position in the digital elevation map (DEM), are presented.
Journal ArticleDOI
Experimental evaluation of nonlinear feedback and feedforward control schemes for manipulators
Pradeep K. Khosla,Takeo Kanade +1 more
TL;DR: The experimental results of the real-time performance of the model-based control algorithms are presented and the importance of including the off-diagonal terms of the manipulator inertia matrix in the torque compu tation is underscore.
Journal ArticleDOI
Computer vision for assistive technologies
TL;DR: An original "task oriented" way to categorize the state of the art of the AT works has been introduced that relies on the split of the final assistive goals into tasks that are then used as pointers to the works in literature in which each of them has been used as a component.
Proceedings ArticleDOI
Dual-state parametric eye tracking
TL;DR: This work develops a dual-state model-based system for tracking eye features that uses convergent tracking techniques and shows how it can be used to detect whether the eyes are open or closed, and to recover the parameters of the eye model.
Proceedings ArticleDOI
Discriminative cluster analysis
TL;DR: This paper proposes a new clustering algorithm called Discriminative Cluster Analysis (DCA), which jointly performs dimensionality reduction and clustering and shows the benefits of clustering in a low dimensional discriminative space rather than in the PC space (generative).