ICRA 2011 Paper Abstract


Paper WeA214.4

Kootstra, Gert (Royal Institute of Technology (KTH), Stockholm), Kragic, Danica (KTH)

Fast and Bottom-Up Object Detection, Segmentation, and Evaluation using Gestalt Principles

Scheduled for presentation during the Regular Sessions "Computer Vision for Robotics and Automation II" (WeA214), Wednesday, May 11, 2011, 10:50−11:05, Room 5J

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on July 5, 2020

Keywords Computer Vision for Robotics and Automation, Recognition, Biologically-Inspired Robots


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.



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