CVNet - visual recognition

CVNet (cvnet@skivs.ski.org)
Tue, 28 Jan 97 01:15:25 PST

Date: Sat, 25 Jan 1997 00:36:49 -0500
From: Rajesh Rao <rao@cs.rochester.edu>
To: connectionists@cs.cmu.edu, neuron@cattell.psych.upenn.edu,
comp-neuro@smaug.bbb.caltech.edu, submission@vislist.com,
cvnet@skivs.ski.org
Subject: Technical Report: Visual recognition and robust Kalman filters

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@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/

===========================================================================

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 ------------------------------------------------------------------------- Anonymous ftp instructions:

>ftp ftp.cs.rochester.edu Connected to anon.cs.rochester.edu. 220 anon.cs.rochester.edu FTP server (Version wu-2.4(3)) ready.

Name: [type 'anonymous' here] 331 Guest login ok, send your complete e-mail address as password.

Password: [type your e-mail address here]

ftp> cd /pub/u/rao/papers/ ftp> get robust.ps ftp> bye