Exploring objects for recognition in the real world
AbstractPerception 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 InfoIf 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.
Bibliographic InfoPaper provided by University College London in its series Open Access publications from University College London with number http://discovery.ucl.ac.uk/18739/.
Date of creation: Dec 2007
Date of revision:
Contact details of provider:
Web page: http://www.ucl.ac.uk
You can help add them by filling out this form.
reading list or among the top items on IDEAS.Access and download statisticsgeneral information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Kieron Jones).
If references are entirely missing, you can add them using this form.