DSpace at KIST: Categorical Object Recognition Method Robust to Scale Changes Using Depth Data From an RGB-D Sensor

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DC Field	Value	Language
dc.contributor.author	Yoo, Ju Han	-
dc.contributor.author	Park, Sung-Kee	-
dc.contributor.author	Kim, Dong Hwan	-
dc.date.accessioned	2024-01-19T11:40:31Z	-
dc.date.available	2024-01-19T11:40:31Z	-
dc.date.created	2022-03-01	-
dc.date.issued	2015-01	-
dc.identifier.issn	2158-3994	-
dc.identifier.uri	https://pubs.kist.re.kr/handle/201004/115080	-
dc.description.abstract	We propose a new categorical object recognition algorithm robust to scale changes. We first partition an input image into k regions by using depth data from an RGB-D sensor, and then we estimate the object scale for each partitioned region. Finally, scaled model is applied to recognize the object.	-
dc.language	English	-
dc.publisher	IEEE	-
dc.title	Categorical Object Recognition Method Robust to Scale Changes Using Depth Data From an RGB-D Sensor	-
dc.type	Conference	-
dc.description.journalClass	1	-
dc.identifier.bibliographicCitation	IEEE International Conference on Consumer Electronics (ICCE), pp.98 - 99	-
dc.citation.title	IEEE International Conference on Consumer Electronics (ICCE)	-
dc.citation.startPage	98	-
dc.citation.endPage	99	-
dc.citation.conferencePlace	US	-
dc.citation.conferencePlace	Las Vegas, NV	-
dc.citation.conferenceDate	2015-01-09	-
dc.relation.isPartOf	2015 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS (ICCE)	-
dc.identifier.wosid	000371904100042	-
dc.identifier.scopusid	2-s2.0-84936078879	-

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