Advanced Search
MyIDEAS: Login

Exploring objects for recognition in the real world

Contents:

Author Info

  • Kootstra, G.
  • Ypma, J.
  • De Boer, B.
Registered author(s):

    Abstract

    Perception in natural systems is a highly active process. In this paper, we adopt the strategy of natural systems to explore objects for 3D object recognition using robots. The exploration of objects enables the system to learn objects from different viewpoints, which is essential for 3D bject recognition. Exploration furthermore simplifies the segmentation of the object from its background, which is important for object learning in real-world environments, which are usually highly cluttered. We use the Scale Invariant Feature Transform (SIFT) as the basis for our object recognition system. We discuss our active vision approach to learn and recognize 3D objects in cluttered and uncontrolled environments. Furthermore, we propose a model to reduce the number of SIFT keypoints stored in the object database. It is a known drawback of SIFT that the computational complexity of the algorithm increases rapidly with the number of keypoints. We discuss the use of a growing-when-required (GWR) network, which is based on the Kohonen Self Organizing Feature Map, for efficient clustering of the keypoints. The results show successful learning of 3D objects in a cluttered and uncontrolled environment. Moreover, the GWR-network strongly reduces the number of keypoints.

    Download Info

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
    File URL: http://discovery.ucl.ac.uk/18739/1/18739.pdf
    Download Restriction: no

    Bibliographic Info

    Paper provided by University College London in its series Open Access publications from University College London with number http://discovery.ucl.ac.uk/18739/.

    as in new window
    Length:
    Date of creation: Dec 2007
    Date of revision:
    Handle: RePEc:ner:ucllon:http://discovery.ucl.ac.uk/18739/

    Contact details of provider:
    Web page: http://www.ucl.ac.uk

    Related research

    Keywords:

    References

    No references listed on IDEAS
    You can help add them by filling out this form.

    Citations

    Lists

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    Statistics

    Access and download statistics

    Corrections

    When requesting a correction, please mention this item's handle: RePEc:ner:ucllon:http://discovery.ucl.ac.uk/18739/

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Kieron Jones).

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.