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Content Provider | IEEE Xplore Digital Library |
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Author | Bo, Liefeng Ren, Xiaofeng Fox, Dieter |
Copyright Year | 2011 |
Description | Author affiliation: Intel Labs Seattle, WA 98105, USA (Ren, Xiaofeng) || Department of Computer Science & Engineering, University of Washington, Seattle, 98195, USA (Bo, Liefeng; Fox, Dieter) |
Abstract | Consumer depth cameras, such as the Microsoft Kinect, are capable of providing frames of dense depth values at real time. One fundamental question in utilizing depth cameras is how to best extract features from depth frames. Motivated by local descriptors on images, in particular kernel descriptors, we develop a set of kernel features on depth images that model size, 3D shape, and depth edges in a single framework. Through extensive experiments on object recognition, we show that (1) our local features capture different aspects of cues from a depth frame/view that complement one another; (2) our kernel features significantly outperform traditional 3D features (e.g. Spin images); and (3) we significantly improve the capabilities of depth and RGB-D (color+depth) recognition, achieving 10–15% improvement in accuracy over the state of the art. |
Starting Page | 821 |
Ending Page | 826 |
File Size | 1287110 |
Page Count | 6 |
File Format | |
ISBN | 9781612844541 |
ISSN | 21530858 |
e-ISBN | 9781612844565 |
DOI | 10.1109/IROS.2011.6095119 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2011-09-25 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Kernel Three dimensional displays Vectors Shape Object recognition Feature extraction Principal component analysis |
Content Type | Text |
Resource Type | Article |
Subject | Control and Systems Engineering Computer Science Applications Computer Vision and Pattern Recognition Software |
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