Ali, Haider und Shafait, Faisal und Giannakidou, Eirini und Vakali, Athena und Figueroa, Nadia und Varvadoukas, Theodoros und Mavridis, Nikolaos (2014) Contextual object category recognition for RGB-D scene labeling. Robotics and Autonomous Systems, 62 (2), 241 - 256. Elsevier. doi: 10.1016/j.robot.2013.10.001. ISSN 0921-8890.
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Offizielle URL: http://www.sciencedirect.com/science/article/pii/S0921889013001929
Kurzfassung
Recent advances in computer vision on the one hand, and imaging technologies on the other hand, have opened up a number of interesting possibilities for robust 3D scene labeling. This paper presents contributions in several directions to improve the state-of-the-art in RGB-D scene labeling. First, we present a novel combination of depth and color features to recognize different object categories in isolation. Then, we use a context model that exploits detection results of other objects in the scene to jointly optimize labels of co-occurring objects in the scene. Finally, we investigate the use of social media mining to develop the context model, and provide an investigation of its convergence. We perform thorough experimentation on both the publicly available RGB-D Dataset from the University of Washington as well as on the {NYU} scene dataset. An analysis of the results shows interesting insights about contextual object category recognition, and its benefits.
elib-URL des Eintrags: | https://elib.dlr.de/97147/ | ||||||||||||||||||||||||||||||||
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Dokumentart: | Zeitschriftenbeitrag | ||||||||||||||||||||||||||||||||
Titel: | Contextual object category recognition for RGB-D scene labeling | ||||||||||||||||||||||||||||||||
Autoren: |
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Datum: | 1 Februar 2014 | ||||||||||||||||||||||||||||||||
Erschienen in: | Robotics and Autonomous Systems | ||||||||||||||||||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||||||||||||||||||
Open Access: | Nein | ||||||||||||||||||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||||||||||||||||||
In ISI Web of Science: | Ja | ||||||||||||||||||||||||||||||||
Band: | 62 | ||||||||||||||||||||||||||||||||
DOI: | 10.1016/j.robot.2013.10.001 | ||||||||||||||||||||||||||||||||
Seitenbereich: | 241 - 256 | ||||||||||||||||||||||||||||||||
Verlag: | Elsevier | ||||||||||||||||||||||||||||||||
ISSN: | 0921-8890 | ||||||||||||||||||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||||||||||||||||||
Stichwörter: | Object recognition; Contextual modeling; RGB-D scenes; Social media; 3D scene labeling | ||||||||||||||||||||||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||||||||||||||||||||||
HGF - Programm: | Raumfahrt | ||||||||||||||||||||||||||||||||
HGF - Programmthema: | Technik für Raumfahrtsysteme | ||||||||||||||||||||||||||||||||
DLR - Schwerpunkt: | Raumfahrt | ||||||||||||||||||||||||||||||||
DLR - Forschungsgebiet: | R SY - Technik für Raumfahrtsysteme | ||||||||||||||||||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | R - Vorhaben Multisensorielle Weltmodellierung (alt) | ||||||||||||||||||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||||||||||||||||||
Institute & Einrichtungen: | Institut für Robotik und Mechatronik (ab 2013) | ||||||||||||||||||||||||||||||||
Hinterlegt von: | Ali, Haider | ||||||||||||||||||||||||||||||||
Hinterlegt am: | 09 Jul 2015 10:25 | ||||||||||||||||||||||||||||||||
Letzte Änderung: | 06 Nov 2023 14:36 |
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