ROBUST ONLINE SEGMENTATION OF UNKNOWN OBJECTS FOR MOBILE ROBOTS

Xin Wang, Maja Rudinac, Pieter P. Jonker

Abstract

In this paper we present a novel vision system for object-driven and online learning based segmentation of unknown objects in a scene. The main application of this system is for mobile robots exploring unknown environments, where unknown objects need to be inspected and segmented from multiple viewpoints. In an initial step, objects are detected using a bottom-up segmentation method based on salient information. The cluster with the most salient points is assumed to be the most dominant object in the scene and serves as an initial model for online segmentation. Then the dominant object is tracked by a Lucas-Kanade tracker and the object model is constantly updated and learned online based on Random Forests classifier. To refine the model a two-step object segmentation using Gaussian Mixture Models and graph cuts is applied. As a result, the detailed contour information of the dominant unknown object is obtained and can further be used for object grasping and recognition. We tested our system in very challenging conditions with multiple identical objects, severe occlusions, illumination changes and cluttered background and acquired very promising results. In comparison with other methods, our system works online and requires no input from users.

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Paper Citation


in Harvard Style

Wang X., Rudinac M. and P. Jonker P. (2012). ROBUST ONLINE SEGMENTATION OF UNKNOWN OBJECTS FOR MOBILE ROBOTS . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012) ISBN 978-989-8565-03-7, pages 365-374. DOI: 10.5220/0003866803650374


in Bibtex Style

@conference{visapp12,
author={Xin Wang and Maja Rudinac and Pieter P. Jonker},
title={ROBUST ONLINE SEGMENTATION OF UNKNOWN OBJECTS FOR MOBILE ROBOTS},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012)},
year={2012},
pages={365-374},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003866803650374},
isbn={978-989-8565-03-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012)
TI - ROBUST ONLINE SEGMENTATION OF UNKNOWN OBJECTS FOR MOBILE ROBOTS
SN - 978-989-8565-03-7
AU - Wang X.
AU - Rudinac M.
AU - P. Jonker P.
PY - 2012
SP - 365
EP - 374
DO - 10.5220/0003866803650374