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 Article
Human Face Detection in Visual Scenes
TL;DR: A neural network-based face detection system that uses a bootstrap algorithm for training, which adds false detections into the training set as training progresses, and has better performance in terms of detection and false-positive rates than other state-of-the-art face detection systems.
Journal ArticleDOI
Vision and Navigation for the Carnegie-Mellon Navlab
TL;DR: By reading vision and navigation the carnegie mellon navlab, you can take more advantages with limited budget.
Journal ArticleDOI
Intelligent access to digital video: Informedia project
TL;DR: Carnegie Mellon's Informedia Digital Video Library project will establish a large, on-line digital video library featuring full-content and knowledge-based search and retrieval, and focused the work on two corpuses.
Book ChapterDOI
A Correlation-Based Approach to Robust Point Set Registration
Yanghai Tsin,Takeo Kanade +1 more
TL;DR: The point set registration problem is defined as finding the maximum kernel correlation configuration of the the two point sets to be registered, and the new registration method has intuitive interpretations, simple to implement algorithm and easy to prove convergence property.
Proceedings ArticleDOI
Probabilistic modeling of local appearance and spatial relationships for object recognition
H. Schneiderman,Takeo Kanade +1 more
TL;DR: An algorithm for object recognition that explicitly models and estimated the posterior probability function, P(object/image) in closed form is described, which captures the joint statistics of local appearance and position on the object as well as the statistics ofLocal appearance in the visual world at large.