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Sensors 2017, 17(3), 451;

An Effective 3D Shape Descriptor for Object Recognition with RGB-D Sensors

School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
Author to whom correspondence should be addressed.
Academic Editor: Fabrizio Lamberti
Received: 28 December 2016 / Revised: 20 February 2017 / Accepted: 20 February 2017 / Published: 24 February 2017
(This article belongs to the Section Physical Sensors)
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RGB-D sensors have been widely used in various areas of computer vision and graphics. A good descriptor will effectively improve the performance of operation. This article further analyzes the recognition performance of shape features extracted from multi-modality source data using RGB-D sensors. A hybrid shape descriptor is proposed as a representation of objects for recognition. We first extracted five 2D shape features from contour-based images and five 3D shape features over point cloud data to capture the global and local shape characteristics of an object. The recognition performance was tested for category recognition and instance recognition. Experimental results show that the proposed shape descriptor outperforms several common global-to-global shape descriptors and is comparable to some partial-to-global shape descriptors that achieved the best accuracies in category and instance recognition. Contribution of partial features and computational complexity were also analyzed. The results indicate that the proposed shape features are strong cues for object recognition and can be combined with other features to boost accuracy. View Full-Text
Keywords: shape descriptor; category recognition; instance recognition; RGB-D sensors shape descriptor; category recognition; instance recognition; RGB-D sensors

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Liu, Z.; Zhao, C.; Wu, X.; Chen, W. An Effective 3D Shape Descriptor for Object Recognition with RGB-D Sensors. Sensors 2017, 17, 451.

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