Video sequence learning and recognition via dynamic SOM

Qiong Liu, Yong Rui, Thomas S Huang, Stephen E Levinson

Research output: Contribution to conferencePaper

Abstract

Information contained in the video sequences is crucial for an autonomous robot or a computer to learn and respond to its surrounding environment. In the past, robot vision is mainly concentrated on still image processing and small `image cube' processing. Continuous video sequence learning and recognition is rarely addressed in the literature due to its high requirement on dynamic processing. In this paper, we propose a novel neural network structure called Dynamic Self-Organizing Map (DSOM) for video sequence processing. The proposed technique has been tested on simulation data sets, and the results validate its learning/recognition ability.

Original languageEnglish (US)
Pages93-97
Number of pages5
StatePublished - Dec 1 1999
EventInternational Conference on Image Processing (ICIP'99) - Kobe, Jpn
Duration: Oct 24 1999Oct 28 1999

Other

OtherInternational Conference on Image Processing (ICIP'99)
CityKobe, Jpn
Period10/24/9910/28/99

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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    Liu, Q., Rui, Y., Huang, T. S., & Levinson, S. E. (1999). Video sequence learning and recognition via dynamic SOM. 93-97. Paper presented at International Conference on Image Processing (ICIP'99), Kobe, Jpn, .