Where-what network with CUDA: general object recognition and location in complex backgrounds

Authors:
Yuekai Wang;Xiaofeng Wu;Xiaoying Song;Wengqiang Zhang;Juyang Weng
Affiliations:
Department of Electronic Engineering, Fudan University, Shanghai, China;Department of Electronic Engineering, Fudan University, Shanghai, China and Ventural Laboratory, Kyoto Institute of Technology, Kyoto, Japan;School of Computer Science, Fudan University, Shanghai, China;School of Computer Science, Fudan University, Shanghai, China;School of Computer Science, Fudan University, Shanghai, China and Department of Computer Science and Engineering, Michigan State University, East Lansing, MI
Venue:
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Year:
2011

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Abstract

An effective framework for general object recognition and localization from complex backgrounds had not been found till the brain-inspiredWhere-What Network (WWN) series by Weng and coworkers. This paper reports two advances along this line. One is the automatic adaptation of the receptive field of each neuron to disregard input dimensions that arise from backgrounds but without a handcrafted object model, since the initial hexagonal receptive field does not fit well the contour of the automatically assigned object view. The other is the hierarchical parallelization technique and its implementation on the GPU-based accelerator using the CUDA parallel language. The experimental results showed that automatic adaptation of the receptive fields led to improvements in the recognition rate. The hierarchical parallelization technique has achieved a speedup of 16 times compared to the C program. This speed-up was employed on the Haibao Robot displayed at the World Expo, Shanghai 2010.