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.
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Book
High Performance Embedded Architectures and Compilers
TL;DR: A new book enPDFd high performance embedded architectures and compilers that can be a new way to explore the knowledge and one thing to always remember in every reading time, even step by step is shown.
Proceedings ArticleDOI
Coplanar Shadowgrams for Acquiring Visual Hulls of Intricate Objects
TL;DR: A practical approach to SFS is presented using a novel technique called coplanar shadowgram imaging, that allows us to use dozens to even hundreds of views for visual hull reconstruction, and yields novel geometric properties that are not possible in traditional multi-view camera- based imaging systems.
Book ChapterDOI
Classification-Driven Pathological Neuroimage Retrieval Using Statistical Asymmetry Measures
TL;DR: A set of novel image features are computed to quantify the statistical distributions of approximate bilateral asymmetry of normal and pathological human brains and this selected feature subset is used as indexing features to retrieve medically similar images under a semantic-based image retrieval framework.
The Phoenix Image Segmentation System: Description and Evaluation
TL;DR: This report summarizes application for which PHOENIX is suited, the history and nature of the algorithm, details of the Testbed implementation, the manner in which PH oenIX is invoked and controlled, the type of results that can be expected, and suggestions for further development.
Proceedings ArticleDOI
Linear motion estimation for systems of articulated planes
TL;DR: The explicit application of articulation constraints for estimating the motion of a system of planes is described, relating articulations to the relative homography between planes and showing that for affine cameras, these articulations translate into linear equality constraints on a linear least squares system, yielding accurate and numerically stable estimates of motion.