Journal article
Spatial hierarchical analysis deep neural network for RGB-D object recognition
Image and Video Technology, Vol.11994, pp.183-193
2020
Abstract
Deep learning based object recognition methods have achieved unprecedented success in the recent years. However, this level of success is yet to be achieved on multimodal RGB-D images. The latter can play an important role in several computer vision and robotics applications. In this paper, we present spatial hierarchical analysis deep neural network, called ShaNet, for RGB-D object recognition. Our network consists of convolutional neural network (CNN) and recurrent neural network (RNNs) to analyse and learn distinctive and translationally invariant features in a hierarchical fashion. Unlike existing methods, which employ pre-trained models or rely on transfer learning, our proposed network is trained from scratch on RGB-D data. The proposed model has been tested on two different publicly available RGB-D datasets including Washington RGB-D and 2D3D object dataset. Our experimental results show that the proposed deep neural network achieves superior performance compared to existing RGB-D object recognition methods.
Details
- Title
- Spatial hierarchical analysis deep neural network for RGB-D object recognition
- Authors/Creators
- S.A.A. Shah (Author/Creator)
- Publication Details
- Image and Video Technology, Vol.11994, pp.183-193
- Publisher
- Springer Verlag
- Identifiers
- 991005540328907891
- Copyright
- © 2020 Springer Nature Switzerland AG
- Murdoch Affiliation
- Information Technology, Mathematics and Statistics
- Language
- English
- Resource Type
- Journal article
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