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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Proceedings ArticleDOI
Convolutional Pose Machines
TL;DR: In this paper, a convolutional network is incorporated into the pose machine framework for learning image features and image-dependent spatial models for the task of pose estimation, which can implicitly model long-range dependencies between variables in structured prediction tasks such as articulated pose estimation.
Journal ArticleDOI
Recognizing action units for facial expression analysis
TL;DR: An Automatic Face Analysis (AFA) system to analyze facial expressions based on both permanent facial features and transient facial features in a nearly frontal-view face image sequence and Multistate face and facial component models are proposed for tracking and modeling the various facial features.
A System for Video Surveillance and Monitoring
Robert T. Collins,Alan J. Lipton,Takeo Kanade,Hironobu Fujiyoshi,David Duggins,Yanghai Tsin,David Tolliver,Nobuyoshi Enomoto,Osamu Hasegawa,Peter J. Burt,Lambert Ernest Wixson +10 more
TL;DR: An overview of theVSAM system, which uses multiple, cooperative video sensors to provide continuous coverage of people and vehicles in a cluttered environment, is presented.
Journal ArticleDOI
Limits on super-resolution and how to break them
Simon Baker,Takeo Kanade +1 more
TL;DR: This work derives a sequence of analytical results which show that the reconstruction constraints provide less and less useful information as the magnification factor increases, and proposes a super-resolution algorithm which attempts to recognize local features in the low-resolution images and then enhances their resolution in an appropriate manner.
Journal ArticleDOI
Multi-PIE
TL;DR: This paper introduces the database, describes the recording procedure, and presents results from baseline experiments using PCA and LDA classifiers to highlight similarities and differences between PIE and Multi-PIE.