Technical Report: Visual recognition and robust Kalman filters

[Rajesh Rao]

The following paper on appearance-based visual recognition and robust
Kalman filtering is now available for retrieval via ftp.

Comments and suggestions welcome (This message has been cross-posted -
my apologies to those who received it more than once).

-- 
Rajesh Rao                       Internet: rao at cs.rochester.edu
Dept. of Computer Science        VOX:  (716) 275-2527              
University of Rochester          FAX:  (716) 461-2018
Rochester  NY  14627-0226        WWW:  http://www.cs.rochester.edu/u/rao/

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     Robust Kalman Filters for Prediction, Recognition, and Learning

			   Rajesh P.N. Rao
                   Department of Computer Science
                      University of Rochester
                      Rochester, NY 14627-0226

			Technical Report 645
			   December, 1996

  Using results from the field of robust statistics, we derive a class
  of Kalman filters that are robust to structured and unstructured
  noise in the input data stream. Each filter from this class
  maintains robust optimal estimates of the input process's hidden
  state by allowing the measurement covariance matrix to be a
  non-linear function of the prediction errors. This endows the filter
  with the ability to reject outliers in the input stream.
  Simultaneously, the filter also learns an internal model of input
  dynamics by adapting its measurement and state transition matrices
  using two additional Kalman filter-based adaptation rules.  We
  present experimental results demonstrating the efficacy of such
  filters in mediating appearance-based segmentation and recognition
  of objects and image sequences in the presence of varying degrees of
  occlusion, clutter, and noise.


Retrieval information:

FTP-host:       ftp.cs.rochester.edu
FTP-pathname:   /pub/u/rao/papers/robust.ps.Z
URL:            ftp://ftp.cs.rochester.edu/pub/u/rao/papers/robust.ps.Z

15 pages; 296K compressed, 1015K uncompressed
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