We are currently experiencing intermittent issues impacting performance. We apologize for the inconvenience.
By Topic

Fast online video image sequence recognition with statistical methods

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Schuster, M. ; Dept. of Comput. Sci., Gerhard-Mercator-Univ., Duisburg, Germany ; Rigoll, G.

In this paper a fast method to recognize image sequences is presented. It is based on a discrete statistical model consisting of a vector quantizer and a special probabilistic neural net giving an estimation for the a posteriori probability P(SEQUENCE|DATA), which allows to classify image sequences without applying rules depending on the content of the sequence. The simple feature extraction also allows the classification with discrete hidden Markov models. As an application we present results from a test conducted for the classification of various gestures done by human beings in front of a video camera for both classification methods, which gave promising recognition results in real time

Published in:

Acoustics, Speech, and Signal Processing, 1996. ICASSP-96. Conference Proceedings., 1996 IEEE International Conference on  (Volume:6 )

Date of Conference:

7-10 May 1996