creators_name: Arsenio, Artur creators_name: Fitzpatrick, Paul creators_name: Kemp, Charles C. creators_name: Metta, Giorgio editors_name: Prince, Christopher G. editors_name: Berthouze, Luc editors_name: Kozima, Hideki editors_name: Bullock, Daniel editors_name: Stojanov, Georgi editors_name: Balkenius, Christian type: confpaper datestamp: 2004-02-12 lastmod: 2011-03-11 08:55:25 metadata_visibility: show title: The Whole World in Your Hand: Active and Interactive Segmentation ispublished: pub subjects: comp-sci-mach-vis subjects: comp-sci-art-intel subjects: comp-sci-robot full_text_status: public keywords: object segmentation, computer vision, robotic system, wearable system abstract: Object segmentation is a fundamental problem in computer vision and a powerful resource for development. This paper presents three embodied approaches to the visual segmentation of objects. Each approach to segmentation is aided by the presence of a hand or arm in the proximity of the object to be segmented. The first approach is suitable for a robotic system, where the robot can use its arm to evoke object motion. The second method operates on a wearable system, viewing the world from a human's perspective, with instrumentation to help detect and segment objects that are held in the wearer's hand. The third method operates when observing a human teacher, locating periodic motion (finger/arm/object waving or tapping) and using it as a seed for segmentation. We show that object segmentation can serve as a key resource for development by demonstrating methods that exploit high-quality object segmentations to develop both low-level vision capabilities (specialized feature detectors) and high-level vision capabilities (object recognition and localization). date: 2003 date_type: published volume: 101 publisher: Lund University Cognitive Studies pagerange: 49-56 refereed: TRUE citation: Arsenio, Artur and Fitzpatrick, Paul and Kemp, Charles C. and Metta, Giorgio (2003) The Whole World in Your Hand: Active and Interactive Segmentation. [Conference Paper] document_url: http://cogprints.org/3329/1/Arsenio.pdf