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

Title
Categorical Object Recognition Method Robust to Scale Changes Using Depth Data From an RGB-D Sensor
Authors
유주한김동환박성기
Keywords
object recognition; category recognition; categorization; scale change; RGB-D sensor
Issue Date
2015-01
Publisher
ICCE (International Conference on Consumer Electronics)
Citation
, 106-107
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.
URI
http://pubs.kist.re.kr/handle/201004/49291
Appears in Collections:
KIST Publication > Conference Paper
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