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
Object Detection Using the Statistics of Parts
H. Schneiderman,Takeo Kanade +1 more
TL;DR: A trainable object detector achieves reliable and efficient detection of human faces and passenger cars with out-of-plane rotation.
Journal ArticleDOI
The Otto Aufranc Award. Image guided navigation system to measure intraoperatively acetabular implant alignment.
Anthony M. DiGioia,Branislav Jaramaz,Mike Blackwell,David A. Simon,F. Morgan,James E. Moody,Constantinos Nikou,Bruce D. Colgan,Cheryl A. Aston,Richard S. LaBarca,Eric Kischell,Takeo Kanade +11 more
TL;DR: These tools successfully were introduced into the clinical practice of surgery and showed the following: There exist unpredictable and large variations in the initial position of patients' pelves on the operating room table and significant pelvic movement during surgery and during intraoperative range of motion testing.
Proceedings ArticleDOI
Video skimming and characterization through the combination of image and language understanding techniques
M.A. Smith,Takeo Kanade +1 more
TL;DR: The goal of this work is to show the utility of integrating language and image understanding techniques for video skimming by extraction of significant information, such as specific objects, audio keywords and relevant video structure.
Proceedings ArticleDOI
Parameter identification of robot dynamics
Pradeep K. Khosla,Takeo Kanade +1 more
TL;DR: In this article, the authors outline the fundamental properties of the Newton-Euler formulation of robot dynamics from the view point of parameter identification and present algorithms for identifying parameters of multi-degrees-of-freedom robotic arm.
Book ChapterDOI
Reconstructing 3d human pose from 2d image landmarks
TL;DR: This work presents an activity-independent method to recover the 3D configuration of a human figure from 2D locations of anatomical landmarks in a single image, leveraging a large motion capture corpus as a proxy for visual memory.