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Using Symmetry to Select Fixation Points for Segmentation
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. (Center for Autonomous Systems)
KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. (Center for Autonomous Systems)
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. (Center for Autonomous Systems)ORCID iD: 0000-0003-2965-2953
2010 (English)In: Proceedings of the 20th International Conference on Pattern Recognition, IEEE , 2010, 3894-3897 p.Conference paper, Published paper (Refereed)
Abstract [en]

For the interpretation of a visual scene, it is important for a robotic system to pay attention to the objects in the scene and segment them from their background. We focus on the segmentation of previously unseen objects in unknown scenes. The attention model therefore needs to be bottom-up and context-free. In this paper, we propose the use of symmetry, one of the Gestalt principles for figure-ground segregation, to guide the robot’s attention. We show that our symmetry-saliency model outperforms the contrast-saliency model, proposed in. The symmetry model performs better in finding the objects of interest and selects a fixation point closer to the center of the object. Moreover, the objects are better segmented from the background when the initial points are selected on the basis of symmetry.

Place, publisher, year, edition, pages
IEEE , 2010. 3894-3897 p.
Keyword [en]
Object Detection, Visual Attention, Object Segmentation
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-47169DOI: 10.1109/ICPR.2010.948Scopus ID: 2-s2.0-78149489613ISBN: 978-1-4244-7542-1 (print)OAI: oai:DiVA.org:kth-47169DiVA: diva2:454634
Conference
the 20th International Conference on Pattern Recognition (ICPR), Istanbul, August 23-26
Projects
SSF RoSy
Note
© 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. QC 20111115Available from: 2011-11-15 Created: 2011-11-07 Last updated: 2012-01-24Bibliographically approved

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