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.
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.
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.
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.
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.
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
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