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Abstract: This thesis establishes a stochastic framework for tracking curves in visual clutter, using a Bayesian random-sampling algorithm. The approach is rooted in ideas from statistics, control theory and computer vision. The problem is to track outlines and features of foreground objects, modelled as curves, as they move in substantial clutter, and to do it at, or close to, video frame-rate. The algorithm, named Condensation, for Conditional density propagation, has recently been derived... (Update)
Context of citations to this paper: More
...one peak (not a ridge) such as the Normal distribution. We give a summary of the algorithm below. For more information, 3] and [11] give an excellent overview of the method. Related algorithms are Importance Sampling [3, 11] and Markov Chain Monte Carlo methods (MCMC)...
...object. Also, the appearance of the targets has been used. For example the Condensation tracking algorithm, proposed by Isari and A. Blake [12][13], uses Point Distribution Models (PDMs) 14] that describes the shape of the target. Brock Gunn and Ellis propose colour appearance...
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1.1: Statistical Models of Visual Shape and Motion - Blake, Bascle, Isard, MacCormick (1998) (Correct)
0.5: Self-Calibration from Image Sequences - Armstrong (1996) (Correct)
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0.5: Tracking Multiple Objects Using the Condensation Algorithm - Koller-Meier, Ade (2001) (Correct)
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BibTeX entry: (Update)
M. Isard. Visual Motion Analysis by Probabilistic Propagation of Conditional Density. PhD thesis, Oxford University, 1998. http://citeseer.ist.psu.edu/isard98visual.html More
@misc{ isard98visual,
author = "M. Isard",
title = "Visual Motion Analysis by Probabilistic Propagation of Conditional Density",
text = "M. Isard. Visual Motion Analysis by Probabilistic Propagation of Conditional
Density. PhD thesis, Oxford University, 1998.",
year = "1998",
url = "citeseer.ist.psu.edu/isard98visual.html" }
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