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| 3D Object Recognition via Convolutional-Recursive Neural Network and Kernel Extreme Learning Machine |
| LIU Yangyang, ZHANG Jun, GAO Xinjian, ZHANG Xudong, GAO Jun |
| School of Computer and Information, Hefei University of Technology, Hefei 230009 |
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Abstract To tackle the issues of depth quality and non-linear classification in the large-scale RGB-D dataset, a 3D object recognition method is designed on the basis of convolutional-recursive neural network(CNN-RNN) and kernel extreme learning machine(KELM). Firstly, a depth coding algorithm is introduced to correct the numerical losses and noises in the original depth cue and unify the point cloud into the standard angle. And the original depth and the encoded depth are fused as the new depth cue. Secondly,multi-cue hierarchical features are learned using CNN-RNN. Meanwhile, the two-way spatial pyramid pooling method is exploited for each cue. Finally, KELM is constructed as the classifier to recognize 3D objects. The experimental results demonstrate the proposed method effectively improves the 3D object recognition accuracy and the classification efficiency.
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Received: 27 July 2017
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| Fund:Supported by National Natural Science Foundation of China(No.61403116), China Postdoctoral Science Foundation(No.2014M560507), Fundamental Research Funds for the Central Universities(No.JZ2016HGBZ0762,JZ2016HGTB0721) |
About author:: (LIU Yangyang, born in 1992, master student. His research interests include intelligent information processing.)
(ZHANG Jun(corresponding author), born in 1984, Ph.D., associate professor. Her research interests include computer vision, pattern recognition and cognitive science.)
(GAO Xinjian, born in 1990, Ph. D. candidate. His research interests include intelligent information processing.)
(ZHANG Xudong, born in 1966, Ph.D., professor. His research interests include intelligent information processing and pattern re-cognition.)
(GAO Jun, born in 1963, Ph.D., professor. His research interests include intelligent information processing and pattern recognition.) |
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2017, Vol. 30 
