Abstract
Generic object recognition is one of the most important fields in the artificial intelligence. Some cortex-like networks for generic object recognition are proposed these years. But most of them concentrated on the discussion about the recognition performance (such as recognition rate, number of objects to be recognized), not the practicability, i.e., implementation with ubiquitous devices and application in real time. This paper reports a try on implementation of a biologically-inspired where-what network (WWN), which integrates object recognition and attention in a single network, via parallelizing the various stages of the network training with CUDA on GPU to shorten the training time. The experiment on HAIBAO Robot exhibited in 2010 Shanghai Expo shows that this optimization can achieve a speedup of almost 16 times compared to the C-based program on an Intel Core 2 DUO 3.00 GHZ CPU in real environments.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Lowe, D.: Object recognition from local scale-invariant features. In: Proc. International Conference on Computer Vision, Kerkyra, September 20-27, vol. 2, pp. 1150–1157 (1999)
Roelfsema, P.R.: Cortical algorigthms for perceptual grouping. Annual Review of Neuroscience 29, 203–227 (2006)
Riesenhuber, M., Poggio, T.: Hierachical models of object recognition in cortex. Nature Neuroscience 2(11), 1019–1025 (1999)
Serre, T., Wolf, L., Bileschi, S., Riesenhuber, M., Poggio, T.: Robust object recognition with cortex-like mechanisms. IEEE Trans. Pattern Analysis and Machine Intelligence 29(3), 411–426 (2007)
Ji, Z., Weng, J., Prokhorov, D.: Where-what network 1: “Where” and “What” assist each other through top-down connections. In: Proc. IEEE International Conference on Development and Learning, Monterey, CA, August 9-12, pp. 61–66 (2008)
Luciw, M., Weng, J.: Where-what network-4: The effect of multiple internal areas. In: Proc. IEEE International Joint Conference on Neural Networks, Ann Arbor, MI, August 18-21, pp. 311–316 (2010)
Ji, Z., Weng, J.: WWN-2: A biologically inspired neural network for concurrent visual attention and recognition. In: Proc. IEEE International Joint Conference on Neural Networks, Barcelona, Spain, July 18-23, pp. 1–8 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag GmbH Berlin Heidelberg
About this chapter
Cite this chapter
Wang, Y., Wu, X., Song, X., Zhang, W., Weng, J. (2012). A Biologically-Inspired Network for Generic Object Recognition Using CUDA. In: Qian, Z., Cao, L., Su, W., Wang, T., Yang, H. (eds) Recent Advances in Computer Science and Information Engineering. Lecture Notes in Electrical Engineering, vol 125. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25789-6_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-25789-6_1
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25788-9
Online ISBN: 978-3-642-25789-6
eBook Packages: EngineeringEngineering (R0)