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Fast and Bottom-Up Object Detection and Segmentation using Gestalt Principles
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. (Center for Autonomous Systems)ORCID iD: 0000-0003-2965-2953
2011 (English)In: Proceedings of the International Conference on Robotics and Automation (ICRA), IEEE , 2011, 3423-3428 p.Conference paper, Published paper (Refereed)
Abstract [en]

In many scenarios, domestic robot will regularly encounter unknown objects. In such cases, top-down knowledge about the object for detection, recognition, and classification cannot be used. To learn about the object, or to be able to grasp it, bottom-up object segmentation is an important competence for the robot. Also when there is top-down knowledge, prior segmentation of the object can improve recognition and classification. In this paper, we focus on the problem of bottom-up detection and segmentation of unknown objects. Gestalt psychology studies the same phenomenon in human vision. We propose the utilization of a number of Gestalt principles. Our method starts by generating a set of hypotheses about the location of objects using symmetry. These hypotheses are then used to initialize the segmentation process. The main focus of the paper is on the evaluation of the resulting object segments using Gestalt principles to select segments with high figural goodness. The results show that the Gestalt principles can be successfully used for detection and segmentation of unknown objects. The results furthermore indicate that the Gestalt measures for the goodness of a segment correspond well with the objective quality of the segment. We exploit this to improve the overall segmentation performance.

Place, publisher, year, edition, pages
IEEE , 2011. 3423-3428 p.
Keyword [en]
Object Detection, Visual Attention, Object Segmentation, Object Evaluation
National Category
Computer Vision and Robotics (Autonomous Systems) Robotics
Identifiers
URN: urn:nbn:se:kth:diva-47172DOI: 10.1109/ICRA.2011.5980410ISI: 000324383402106Scopus ID: 2-s2.0-84871707718ISBN: 978-1-61284-386-5 (print)OAI: oai:DiVA.org:kth-47172DiVA: diva2:454626
Conference
the International Conference on Robotics and Automation (ICRA)
Projects
EU project eSMCs, IST-FP7-IP-270212SSF RoSy
Funder
EU, FP7, Seventh Framework Programme, IST-FP7-IP-270212
Note

© 2011 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 20111115

Available from: 2011-11-15 Created: 2011-11-07 Last updated: 2014-09-30Bibliographically approved

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Kragic, Danica

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