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Fast and Automatic Detection and Segmentation of Unknown Objects
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), Computer Vision and Active Perception, CVAP. 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. 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 10th IEEE-RAS International Conference on Humanoid Robots (Humanoids), IEEE , 2010, 442-447 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper focuses on the fast and automatic detection and segmentation of unknown objects in unknown environments. Many existing object detection and segmentation methods assume prior knowledge about the object or human interference. However, an autonomous system operating in the real world will often be confronted with previously unseen objects. To solve this problem, we propose a segmentation approach named Automatic Detection And Segmentation (ADAS). For the detection of objects, we use symmetry, one of the Gestalt principles for figure-ground segregation to detect salient objects in a scene. From the initial seed, the object is segmented by iteratively applying graph cuts. We base the segmentation on both 2D and 3D cues: color, depth, and plane information. Instead of using a standard grid-based representation of the image, we use super pixels. Besides being a more natural representation, the use of super pixels greatly improves the processing time of the graph cuts, and provides more noise-robust color and depth information. The results show that both the object-detection as well as the object-segmentation method are successful and outperform existing methods.

Place, publisher, year, edition, pages
IEEE , 2010. 442-447 p.
Keyword [en]
Object Detection, Object Segmentation, Visual Attention
National Category
Computer Vision and Robotics (Autonomous Systems) Robotics
Identifiers
URN: urn:nbn:se:kth:diva-47168DOI: 10.1109/ICHR.2010.5686837Scopus ID: 2-s2.0-79851480283ISBN: 978-1-4244-8689-2 (print)OAI: oai:DiVA.org:kth-47168DiVA: diva2:454633
Conference
IEEE-RAS International Conference on Humanoid Robots (Humanoids)
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: 2012-01-16Bibliographically approved

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Kragic, Danica

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