Fast color/texture segmentation for outdoor robots

Morten Rufus Blas, Motilal Agrawal, Aravind Sundaresan, Kurt Konolige

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    797 Downloads (Pure)

    Abstract

    We present a fast integrated approach for online segmentation of images for outdoor robots. A compact color and texture descriptor has been developed to describe local color and texture variations in an image. This descriptor is then used in a two-stage fast clustering framework using K-means to perform online segmentation of natural images. We present results of applying our descriptor for segmenting a synthetic image and compare it against other state-of-the-art descriptors. We also apply our segmentation algorithm to the task of detecting natural paths in outdoor images. The whole system has been demonstrated to work online alongside localization, 3D obstacle detection, and planning.
    Original languageEnglish
    Title of host publicationProceedings of 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems
    PublisherIEEE
    Publication date2008
    Pages4078-4085
    ISBN (Print)9781424420575
    DOIs
    Publication statusPublished - 2008
    EventInternational Conference on Intelligent Robots and Systems - Nice, France
    Duration: 22 Sept 200826 Sept 2008
    http://iros2008.inria.fr/

    Conference

    ConferenceInternational Conference on Intelligent Robots and Systems
    Country/TerritoryFrance
    CityNice
    Period22/09/200826/09/2008
    Internet address

    Bibliographical note

    Copyright: 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

    Fingerprint

    Dive into the research topics of 'Fast color/texture segmentation for outdoor robots'. Together they form a unique fingerprint.

    Cite this