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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.

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Journal ArticleDOI

Reconstruction of a Scene with Multiple Linearly Moving Objects

Mei Han, +1 more
- 21 Sep 2004 - 
TL;DR: An algorithm to recover the scene structure, the trajectories of the moving objects and the camera motion simultaneously given a monocular image sequence is described and a unified geometrical representation of the static scene and theMoving objects is proposed.
Proceedings ArticleDOI

Creating 3D models with uncalibrated cameras

Mei Han, +1 more
TL;DR: A factorization-based method to recover 3D models from multiple perspective views with uncalibrated cameras using a bilinear factorization algorithm to generate the Euclidean reconstruction and the intrinsic parameters, assuming zero skews.
Journal Article

A Head-Wearable Short-Baseline Stereo System for the Simultaneous Estimation of Structure and Motion

Takeo Kanade
- 01 Jan 2011 - 
TL;DR: A short-baseline real-time stereo vision system that is capable of the simultaneous and robust estimation of the ego-motion and of the 3D structure and the independent motion of thousands of points of the environment and can be used to augment the perception of the user in complex dynamic environments.
Proceedings ArticleDOI

Classifying human motion quality for knee osteoarthritis using accelerometers

Portia E. Taylor, +3 more
TL;DR: Methods for assessment of exercise quality using body-worn tri-axial accelerometers will form the basis for an at-home rehabilitation device that will recognize errors in patient exercise performance, provide appropriate feedback on the performance, and motivate the patient to continue the prescribed regimen.
Proceedings ArticleDOI

Mitosis sequence detection using hidden conditional random fields

A-A Liu, +2 more
TL;DR: A fully-automated mitosis event detector using hidden conditional random fields for cell populations imaged with time-lapse phase contrast microscopy that achieved 95% precision and 85% recall in very challenging image sequences of multipolar-shaped C3H10T1/2 mesenchymal stem cells is proposed.