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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Book ChapterDOI
Spatio-temporal Event Classification using Time-series Kernel based Structured Sparsity.
TL;DR: This work proposes a Kernel Structured Sparsity method that can handle both the temporal alignment problem and the structured sparse reconstruction within a common framework, and it can rely on simple features.
Journal ArticleDOI
Modeling sensors: toward automatic generation of object recognition program
Katsushi Ikeuchi,Takeo Kanade +1 more
TL;DR: This paper proposes a representation method for sensor detectability and reliability in the configuration space and investigates how to use the proposed sensor model in automatic generation of object recognition programs.
Journal ArticleDOI
Anomaly detection through registration
TL;DR: A system that automatically segments and classifies features in brain MRI volumes using an atlas, a hand-segmented and classified MRI of a normal brain, which is warped in 3-D using a hierarchical deformable matching algorithm until it closely matches the subject.
Book ChapterDOI
Image segmentation using iterated graph cuts based on multi-scale smoothing
TL;DR: The proposed method can segment the regions of an object with a stepwise process from global to local segmentation by iterating the graph-cuts process with Gaussian smoothing using different values for the standard deviation.
Proceedings ArticleDOI
Fast reactive control for illumination through rain and snow
Raoul de Charette,Robert Tamburo,Peter Barnum,Anthony Rowe,Takeo Kanade,Srinivasa G. Narasimhan +5 more
TL;DR: A system that will directly improve driver visibility by controlling illumination in response to detected precipitation is presented, and a proof-of-concept system that can avoid water drops generated in the laboratory is built and evaluated.