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Probabilistic exemplar-based pattern tracking

  • US 7,167,578 B2
  • Filed: 12/09/2005
  • Issued: 01/23/2007
  • Est. Priority Date: 02/22/2002
  • Status: Expired due to Fees
First Claim
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1. A method for automatic probabilistic pattern tracking comprising steps for:

  • automatically learning a set of exemplars from at least one set of training data;

    clustering the exemplars into more than one cluster of exemplars, with each cluster having a representative exemplar at a cluster center;

    generating an observation likelihood function for each exemplar cluster based on a computed distance between the exemplars in each cluster;

    providing the exemplar clusters, observation likelihood functions, and target data to a probabilistic tracking function; and

    probabilistically tracking at least one pattern in the target data by using the exemplar clusters, observation likelihood functions, and target data to predict at least one target state.

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