Proceedings Paper
Bayesian estimation of smooth object motion using data from direction-sensitive velocity sensors| Format | Member Price | Non-Member Price |
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Paper Abstract
A two-stage process involving Bayesian estimates of smooth velocity vectors is used to detect physical object movements in an image sequence containing noisy background motions. This process computes the probability that a velocity vector is `smooth' with respect to a vector in the previous frame. Those vectors with a high probability are assembled into `paths' and paths longer than a threshold are retained. When this process is applied to the output of a velocity- sensitive network, random movements from the background are filtered out from the sequence, retaining only the smooth motion vectors.
Paper Details
Date Published: 1 October 1991
PDF: 6 pages
Proc. SPIE 1569, Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision, (1 October 1991); doi: 10.1117/12.48375
Published in SPIE Proceedings Vol. 1569:
Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision
Su-Shing Chen, Editor(s)
PDF: 6 pages
Proc. SPIE 1569, Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision, (1 October 1991); doi: 10.1117/12.48375
Show Author Affiliations
David Yushan Fong, Lockheed Palo Alto Research Labs. (United States)
Carlos A. Pomalaza-Raez, Purdue Univ. (United States)
Published in SPIE Proceedings Vol. 1569:
Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision
Su-Shing Chen, Editor(s)
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