By Topic

Visual scene analysis using relaxation labeling and Embedded Hidden Markov Models for map-based robot navigation

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
$33 $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

3 Author(s)
Alessandro Moro ; DEEI, University degli Studi di Trieste, 34127, Italy ; Enzo Mumolo ; Massimiliano Nolich

A scheme for extracting environment features and performing their interpretation from visual data for mobile robot navigation is presented. Each frame of the low rate image stream acquired by the robot is processed as a separate image. Segmentation of the image is done using a graph-based approach in order to select the regions of interest (ROIs) of the visual scene. ROIs are processed to extract the edges of the objects using relaxation labeling. The obtained image is analyzed using a machine learning approach based on embedded HMMs. Experimental results are presented for an office environment.

Published in:

Information Technology Interfaces, 2008. ITI 2008. 30th International Conference on

Date of Conference:

23-26 June 2008