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Discriminative bit selection hashing in RGB-D based object recognition for robot vision

Lin Feng (School of Innovation and Entrepreneurship, Dalian University of Technology, Dalian, China)
Yang Liu (Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China)
Zan Li (Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China)
Meng Zhang (School of Computer Science and Technology, Dalian University of Technology, Dalian, China)
Feilong Wang (Dalian University of Technology, Dalian, China)
Shenglan Liu (School of Control Science and Engineering, Dalian University of Technology, Dalian, China)

Assembly Automation

ISSN: 0144-5154

Article publication date: 16 October 2018

Issue publication date: 16 April 2019

Abstract

Purpose

The purpose of this paper is to promote the efficiency of RGB-depth (RGB-D)-based object recognition in robot vision and find discriminative binary representations for RGB-D based objects.

Design/methodology/approach

To promote the efficiency of RGB-D-based object recognition in robot vision, this paper applies hashing methods to RGB-D-based object recognition by utilizing the approximate nearest neighbors (ANN) to vote for the final result. To improve the object recognition accuracy in robot vision, an “Encoding+Selection” binary representation generation pattern is proposed. “Encoding+Selection” pattern can generate more discriminative binary representations for RGB-D-based objects. Moreover, label information is utilized to enhance the discrimination of each bit, which guarantees that the most discriminative bits can be selected.

Findings

The experiment results validate that the ANN-based voting recognition method is more efficient and effective compared to traditional recognition method in RGB-D-based object recognition for robot vision. Moreover, the effectiveness of the proposed bit selection method is also validated to be effective.

Originality/value

Hashing learning is applied to RGB-D-based object recognition, which significantly promotes the recognition efficiency for robot vision while maintaining high recognition accuracy. Besides, the “Encoding+Selection” pattern is utilized in the process of binary encoding, which effectively enhances the discrimination of binary representations for objects.

Keywords

Acknowledgements

This study was funded by National Natural Science Foundation of Peoples Republic of China (61672130, 61602082, 91648205), the National Key Scientific Instrument and Equipment Development Project (No. 61627808), the Development of Science and Technology of Guangdong Province Special Fund Project Grants (No. 2016B090910001) and the Open Program of State Key Laboratory of Software Architecture, Item number SKLSAOP1701. All the authors declare that they have no conflict of interest.

Citation

Feng, L., Liu, Y., Li, Z., Zhang, M., Wang, F. and Liu, S. (2019), "Discriminative bit selection hashing in RGB-D based object recognition for robot vision", Assembly Automation, Vol. 39 No. 1, pp. 17-25. https://doi.org/10.1108/AA-03-2018-037

Publisher

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Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited