Cart (Loading....) | Create Account
Close category search window
 

Urban Object Recognition from Informative Local Features

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

3 Author(s)
Fritz, G. ; JOANNEUM RESEARCH Forschungsgesellschaft mbH Institute of Digital Image Processing Wastiangasse 6, A-8010 Graz, Austria gerald.fritz@joanneum.at ; Seifert, C. ; Paletta, L.

Autonomous mobile agents require object recognition for high level interpretation and localization in complex scenes. In urban environments, recognition of buildings might play a dominant role in robotic systems that need object based navigation, that take advantage of visual feedback and multimodal information for self-localization, or that enable association to related information from the identified semantics. We present a new method – the informative local features approach – based on an information theoretic saliency measure that is rapidly derived from a local Parzen window density estimation in feature subspace. From the learning of a decision tree based mapping to informative features, it enables attentive access to discriminative information and thereby significantly speeds up the recognition process. This approach is highly robust with respect to severe degrees of partial occlusion, noise, and tolerant to some changes in scale and illumination. We present performance evaluation on our publicly available reference object database (TSG-20) that demonstrates the efficiency of this approach, case wise even outperforming the SIFT feature approach [1]. Building recognition will be advantageous in various application domains, such as, mobile mapping, unmanned vehicle navigation, and systems for car driver assistance.

Published in:

Robotics and Automation, 2005. ICRA 2005. Proceedings of the 2005 IEEE International Conference on

Date of Conference:

18-22 April 2005

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.