Fast and bottom-up object detection, segmentation, and evaluation using gestalt principles

Gert Kootstra*, Danica Kragic

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paper

16 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2011 IEEE International Conference on Robotics and Automation, ICRA 2011
PublisherIEEE Xplore
Pages3423-3428
Number of pages6
ISBN (Print)9781612843865
DOIs
Publication statusPublished - 1 Dec 2011
Externally publishedYes
Event2011 IEEE International Conference on Robotics and Automation, ICRA 2011 - Shanghai, China
Duration: 9 May 201113 May 2011

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Conference

Conference2011 IEEE International Conference on Robotics and Automation, ICRA 2011
CountryChina
CityShanghai
Period9/05/1113/05/11

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