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Cristian Ferrer Canton
...estimating .... To demonstrate the e®ectiveness of the proposed algorithm, the authors present tracking results of people in a SmartRoom scenario and compare these results with existing methods ...
10 months ago
Videolectures.net
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Kalman filter
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Alexander J. Smola
...computer ...algorithms ...estimate compatibility functions such that the solution of the resulting graph matching problem best matches the expected solution that a human would manually provide. We ...
10 months ago
Videolectures.net
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Graph (mathematics)
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Researchers at CMU have created the first computerized program that can analyze a single photograph and determine where in the world the image was likely taken.
4 months ago
ABC (Pittsburgh)
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...computer ...estimate ...algorithm optimizes these parameters along with the face's internal shape and surface colour to find the best match to the input image. The face model extracted from the ...
5m 16s |
a year ago
YouTube
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3D computer graphics
eigenlaut (YouTube)
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Virginia Tech Computer Vision Target Tracking Algorithm Output: Used a dynamic background subtraction algorithm to localize moving Unmanned Ground Vehicle. Red dot is estimated centroid of UGV.
0m 16s |
11 months ago
YouTube
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BaysianLogik (YouTube)
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Google Tech Talks August 5, 2008 ABSTRACT Estimating geographic information from an image is an excellent, difficult high-level computer vision problem whose time has come. The emergence of vast amounts of geographically-calibrated image data is a great reason for computer vision to start looking globally on the scale of the entire planet! In this paper, we propose a simple algorithm for estimating a distribution over geographic locations from a single image using a purely data-driven scene matching approach. For this task, we will leverage a dataset of over 6 million GPS-tagged images from the Internet. We represent the estimated image location as a probability distribution over the Earth's surface. We quantitatively evaluate our approach in several geolocation tasks and demonstrate encouraging performance (up to 30 times better than chance). We show that geolocation estimates can provide the basis for numerous other image understanding tasks such as population density estimation, land cover estimation or urban/rural classification. Speaker: James Hays James Hays received his B.S. in Computer Science from Georgia Institute of Technology in 2003. He has been a Ph.D. student in Carnegie Mellon University's Computer Science Department since 2003 and is advised by Alexei A. Efros. His research interests are in computer vision and computer graphics, focusing on image understanding and manipulation leveraging massive amounts of data. His research has been supported by a National Science Foundation Graduate Research Fellowship.
47m 26s |
3 months ago
YouTube
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Inheritance (computer science)
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googletechtalks (YouTube)
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